Keywords: Modelling and control of hybrid and discrete event systems
Abstract: Revolutionary computing technologies are driving significant advances in the manufacturing domain. High-fidelity simulations and virtual design environments allow unprecedented opportunities to test and validate systems before they are built, reducing overall design time and cost. These simulations can be run in real-time, in parallel to the physical system, to enable better visibility into plant floor operations. Torrents of data streaming from the both simulations and the factory floor can be collected over high-speed networks, and stored in the cloud, enabling improved analytics, predictions, and performance. This talk will describe how the integration of simulation and plant-floor data can enable new control approaches that are able to optimize the overall performance of manufacturing system operations. Collaborations with industry will be highlighted.

Keywords: Real time optimization and control, Control of renewable energy resources
Abstract: In this paper, a 3-D model of a Magnus effect-based airborne wind energy system adapted from flight dynamics is proposed. The model is derived from first principles where the forces acting on the system are presented. In order to validate our aerodynamic model, a study of lift and drag coefficients is presented for the rotating cylinders in high Reynolds number regime. The proposed 3-D model is validated by simulation, controlling the system in crosswind trajectories. The performance of the system power production is also assessed and compared to a simplified static model.

Keywords: Monitoring and performance assessment, Fault diagnosis and fault tolerant control, Monitoring of product quality and control performance
Abstract: Probabilistic graphical models like Bayesian networks have been widely used in process monitoring and fault diagnosis, however, their application is mostly limited to discrete variables or continuous Gaussian variables due to the difficult in estimation of multivariate joint density. In order to deal with the estimation problem of multivariate joint density for continuous variables, this paper decomposes the graphical model into hierarchical structure so that the problem of joint density estimation can be transferred to estimation of several conditional probability densities and low-dimensional probability densities. The conditional densities can be effectively estimated from data by a nonparametric kernel method and the low-dimensional densities can be estimated using the kernel density estimation (KDE). Based on the estimated densities, process faults can be detected by examining which probability is lower than the cutoff value. Application to the blast furnace ironmaking process is used to illustrate the advantages of the proposed method.

Keywords: Monitoring and performance assessment, Monitoring of product quality and control performance
Abstract: Historical data based index has been a focus method for control loop performance assessment in recent years. However, it has the subjectivity limitation and cannot indicate the cause of performance degradation. In addition, performance assessment based on a single historical data index can easily lead to erroneous results. In this paper, a multi-index control performance assessment method is proposed using historical prediction error based covariance indices to overcome the above limitations. In the proposed method, three covariance indices are firstly constructed to detect the changes in the covariance of output variables calculated from monitored data and the covariance of output variables’ first τ-step prediction error calculated respectively from benchmark data and monitored data. Furthermore, the generalized eigenvalues of these built covariance matrices are analyzed to find the change directions of control performance. The effectiveness of the proposed method is illustrated through case studies on a two-variable numerical system and the Wood Berry distillation column system.

Keywords: Monitoring and performance assessment, Process performance monitoring/statistical process control, Estimation and fault detection
Abstract: In industrial plants, productivity and product quality are often impacted by different types of faults. Specifically, oscillations commonly exist in many close-loop controlled processes. An oscillation generated in a single unit may propagate along process flows and feedback loops, affecting the performance of the entire plant. Therefore, it is critical to diagnose such oscillation-type plant faults and find out the root cause, so as to achieve fast recovery from abnormalities. In recent research, Granger causality (GC) test, which uses a statistical hypothesis test to judge whether a time series is useful in forecasting another, has been adopted to discover the root cause of plant-wide oscillations. However, the conventional GC is based on linear autoregressive (AR) models and cannot accurately handle the nonlinear causal relationship between time series. To solve this problem, a two-step diagnosis approach is proposed in this paper. In the first step, the faulty variables are isolated by the least absolute shrinkage and selection operator (LASSO) based reconstruction analysis method. Then, the nonlinear GC test based on Gaussian process regression (GPR) is conducted on the isolated process variables to discover the path of fault propagation and find out the root cause of the fault.

Keywords: Process control applications, Monitoring and performance assessment, Modeling and simulation of power systems
Abstract: In this work, we present the fundamentals of the estimation of the energy efficiency in an industrial coal boiler based in novel optical combustion diagnostics variables and several machine learning regression methods. The total radiation Radt and flame temperature Tf were considered. The inclusion of those variables allows to increase the overall performance in the estimation of the energy efficiency. The comparison in the performance of the tested methods for regression, suggest that Extreme Learning Machines in combination with Partial Least Squares for regression, lead to the best performance with a Pearson correlation coefficient R = 0.7 in the test data set.

Keywords: Process control applications
Abstract: Bias of data location and increase of data variations are two typical disturbances, which in general simultaneously exist in the fault process. Targeting their different characteristics, a nested-loop fisher discriminant analysis (NeLFDA) algorithm and relative changes (RC) algorithm are effectively combined for analyzing the fault characteristics. For the fault data containing those two faults simultaneously, a combined NeLFDA-RC algorithm is proposed for fault deviations modeling, which is termed as CNR-FD. Fault directions concerning bias of data location are extracted by NeLFDA algorithm and then the fault deviations associated with these directions are removed from the fault data. Then directions concerning increase of data variations are extracted by RC algorithm. These fault directions are used as reconstruction models to characterize each fault class. Online fault diagnosis is then performed by finding the specific reconstruction models that can well remove alarm signals for current sample. Its performance is illustrated using a numerical simulation example and pre-programmed faults from Tennessee Eastman (TE) benchmark process.

Keywords: Real time optimization and control, Industrial applications of process control, Control system design
Abstract: This paper presents a new solution for the design of a controller for a process using exclusively input/output (I/O) data, which enables the overall system to exhibit high control performance including tracking performance and system robustness in case of deterministic additive disturbances. First, a data-driven model is constructed based on deadbeat diagnostic observer/residual generator. Next, a new state, which is equivalent to the integral of the system tracking error, combined with process input and output information is used to define the performance criterion. Minimising this performance criterion using an online control policy gives an optimal feedback and feedforward controller. The effectiveness of the proposed approach for industrial implementations is shown using a DC-motor simulation.

Keywords: Control of distributed systems, Control of renewable energy resources, Smart grids
Abstract: We propose a decentralized control synthesis procedure for stabilizing voltage and frequency in AC Islanded microGrids (ImGs), i.e. electrical networks composed of interconnected Distributed Generation Units (DGUs), power lines and loads. The presented approach enables Plug-and-Play (PnP) operations, meaning that DGUs can be arbitrarily added or removed without compromising the overall ImG stability. The main feature of our approach is that, differently from (Riverso et al., 2015), the proposed PnP design algorithm is line-independent. This implies that (i) the synthesis of each local controller does not require anymore the model of power lines connecting neighboring DGUs, and (ii) whenever a new DGU is plugged-in, DGUs physically connected to it do not have to retune their regulators. Theoretical results are validated by simulating in PSCAD the behavior of a 10-DGUs ImG.

Keywords: Monitoring and performance assessment, Process control applications, Signal processing for FDI
Abstract: This study is motivated by the fact that minimum variance control (MVC) benchmark can be insucient in case of deterministic process inputs, since stochasticty is assumed. Therefore, it is crucial to know the type of disturbance while assessment. In this paper, a new approach is proposed to extend MVC via characterization of disturbance, which require only routine closed loop data and is easy-to-use online. The method can also detect oscillations properly in noisy process measurements. Suggested method uses a decision tree to combine controller performance index (CPI) with correlation coecient which identies the type of disturbance. The algorithm is applied into a renery unit where the root causes of poorly performing loops are known. The results are discussed in the scope of performance monitoring.

Keywords: Monitoring and performance assessment
Abstract: In process industries, it is necessary to maintain user-specified control performance to ensure energy saving and/or desired product quality specifications. This was the main motivation in the development of a performance-driven control scheme based on the minimum variance control law. In that scheme, control parameters are adjusted when the control performance based on the MV-Index (Minimum Variance Index) deteriorates. However, this MV-Index based tuning scheme only takes into account only controller error variance unfortunately with no regard for manipulative variable activity. To circumvent this omission, this paper describes a design of a new performance-driven control system whose control performance assessment scheme takes into account controller error variance as well as the manipulating variable variance. Furthermore, in this newly proposed scheme, control parameters are calculated using a fictitious reference iterative tuning (FRIT) scheme which is one of the data-driven tuning scheme. The effectiveness of the proposed scheme is demonstrated by evaluation on a numerical example.

Keywords: Monitoring and performance assessment, Computational methods for FDI, Estimation and fault detection
Abstract: The presence of faults in large-scale facilities, quite frequently propagates plant-wide due to energy integration and consequently triggers a sequence of alarms, which are known as alarm floods. For the purpose of decision support in the root cause identification of alarm floods, this paper presents a framework to combine causality inference using both process and alarm data. The method includes the following main steps: first, alarm floods are identified from alarm data; second, root cause alarms are detected through cause and effect analysis; third, associated process variables are extracted and causal relations are confirmed. Using this method, the number of alarm tags analyzed for root cause identification is short-listed. Causal relations between the corresponding short-listed process variables are then used to support root cause analysis. The practical utility of the method is demonstrated by application to an industrial dataset for which both process and alarm data are available.

Keywords: Advanced control technology, Process control applications
Abstract: PID controllers have been widely employed in real processes.Since PID parameters strongly affect the control performance, lots of schemes for tuning PID parameters have been proposed.Fixed PID controllers are applied mainly.However, it is impossible to obtain good control performance for time-variant systems by the fixed PID controllers.Therefore, it is important to tune PID parameters in an on-line manner.In addition, especially in process systems, it is difficult to disarrange the systems to identify.Hence, it is important to determine PID parameters directly from the closed-loop operating data.In this paper, in order to overcome these problems, an implicit self-tuning PID control scheme is proposed.In the proposed method, a new parameter tuning law using obtained closed-loop data is introduced, and recursive least squares method is applied to the scheme.This scheme has a reference model which can be designed by users. As a result, system output can track the desired reference model output.The new parameter tuning law can calculate PID gains directly from a closed-loop data, so the proposed method is an implicit scheme.Therefore, a system identification can be avoided.In addition, procedures of the scheme are very simple.Therefore, the calculation cost becomes low.The effectiveness of the control scheme is evaluated by some experimental examples.

Keywords: Modeling of manufacturing operations, Production activity control, Modeling of assembly units
Abstract: In this paper, we develop a practical decision making tool to analyze and detect system bottlenecks for serial production lines with multiple Geometric machines and finite buffer. Systematic approaches are investigated to quantify the overall system performance-the production rate and its interactions with characteristics of individual machines. Two types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems where machine's downtime is significantly longer than the cycle time.

Keywords: Production activity control, Protocols and information communication, Process supervision
Abstract: The current paper develops a pioneering approach to the human factors of picking operations, which are addressed from a cognitive perspective. The model is established through a qualitative methodology, which based on several theories, is articulated and applied to the real situation of a dry foods company. The results combine the cognitive architecture of the operation and its relations with logistic factors to decrease the amount of human error. This model provided evidence for three logistic factors, namely, the picking zone type, the order difficulty level and the experience level; furthermore, from the cognitive perspective, attention and memory.

Keywords: Job and activity scheduling, Modeling of manufacturing operations, Production planning and control
Abstract: In this paper, we integrate two decision problems arising in various applications such as production planning and project management: the project scheduling problem, which consists in scheduling a set of precedence-constrained tasks, where each task requires executing a set of skills to be performed, and the workforce allocation problem which includes assigning workers as scarce resources to the skills of each task. These two problems are interrelated as the tasks durations are not predefined, but depend on the number of workers assigned to that task as well as their skill levels. We here present a mixed integer linear programming model that considers important real life aspects related to the flexibility in the use of human resources, such as multi-skilled workers whose skill levels are different and measured by their efficiencies. Hence, execution times of the same workload by different workers vary according to these efficiencies. Moreover, the model considers the flexible working time of employees; i.e. the daily and weekly workload of a given worker may vary from one period to another according to the work required. Furthermore, efficient team building is incorporated in this model; i.e. assigning an expert worker and one or more apprentice worker(s) together with the purpose of skill development thanks to knowledge transfer. A numerical example is provided to check the performance of the model.

Keywords: Process supervision, Quality assurance and maintenance
Abstract: With a long history in project management practices, project performance measurement (PPM) offers a wide range of methods and good practices which help project managers to effectively monitor the project and evaluate project progress and results. However, several critical issues remain, such as an unbalanced development of KPIs types or a limited availability of leading Key Performance Indicators (KPIs). On the other hand, systems engineering measurement (SEM) is a more recent discipline, with practices and theories that appeared with the emergence of the systems engineering discipline. Moreover, SEM has been much more developed with some practical research results published in several standards and guides. In particular, SEM does not only use lagging indicators, used to track how things are going but defines methods to promote leading indicators, used as precursors to the direction where the engineering is going; indeed, 18 leading indicators (LIs) were recently proposed, validated, and finally engineered in a practical guidance. Our goal being to improve project performance and success rate, one mean is to improve the project performance measurement, on which decisions rely for project management. To achieve this goal, this paper proposes to extend the project performance measurement of indicators by considering how performance is measured in systems engineering.

Keywords: Job and activity scheduling
Abstract: The paper has the objective of planning the preventive maintenance of a system subject to different failure modes. The preventive maintenance is planned by means of the maximization of the system reliability. The reliability of a system depends on many factors. One of these is the arrangement of the maintenance interventions in a specified time horizon and this is an aspect that has received low attention by literature. A reliability-centered maintenance optimization model is developed in the paper and the optimization can be tackled by means of two methods, according to the fact that the concept of joint replacement is introduced or not.

Keywords: Production planning and control
Abstract: This paper deals with the deterministic single-item procurement planning problem with batch ordering under the buyback contract. We assume a buyback contract with returns of unused products at the end of each period to the supplier and we consider a piecewise procurement cost structure composed by a fixed ordering cost, a variable procurement cost and a fixed cost per batch replenished, incurred by the retailer. This problem is studied under various hypotheses : in the first, the acquisition is made in only full batches, in the second, there is no specific assumption on the acquisition quantity, in the third, the procurement must be in only full batches with a possibility of lost sales, and in the fourth, there is just the option of lost sales for the general case. For these four cases, several optimality properties and polynomial time algorithms are proposed.

Keywords: Logistics in manufacturing, Modeling of manufacturing operations, Production & logistics over manufacturing networking
Abstract: Home Health Care (HHC) companies are widespread in European countries, and aim to serve patients at home to help them recover from illness and injury in a personal environment. Since transportation costs constitute one of the largest forms of expenditure in the Home Health Care industry, it is of importance to research the optimization of the Home Health Care logistics. This paper considers a Home Health Care Routing Problem with stochastic travel and service time, which comes from the logistics practice of the home health care company. A stochastic programming model with recourse (SPR) is proposed, the Hybrid Genetic Algorithm (HGA) and stochastic simulation method are integrated to solve the proposed model. Three series of experiments are carried out to evaluate the model. Firstly, the SPR model is reduced to the deterministic one, experimental results for the related instances validate the effectiveness and efficiency of the proposed HGA. Then HGA and stochastic simulation are integrated to solve the stochastic model. Comparisons between the solutions obtained by stochastic model and deterministic one validate the reasonable and robustness of considering the stochastic travel and service time. This research will help HHC companies to make appropriate decisions when arranging their vehicle routes.

Keywords: Job and activity scheduling, Modelling and decision making in complex systems, Intelligent decision support systems in manufacturing
Abstract: Repetitive work involving manual handling of low loads at high frequency frequently leads to deteriorated posture and movement co-ordination, causing occupational diseases as the most common work-related musculoskeletal disorders (WMSD). Older workers are more predisposed to develop WMSD than younger workers because of their decreased functional capacity. The susceptibility for developing WMSD or injury is related to the difference between the demands of work and the worker’s ability to perform a demanded activity. Current law requires the adoption of risk control measures to eliminate or reduce the exposure of workers to health and safety risk factors. When repetitive handling is unavoidable, job rotation is an effective risk control method to minimize the exposure of workers to the risks of repetitive movements and awkward postures. This paper presents a mathematical model for the design of activity schedules for aged workers exposed to the risk of repetitive work. The aim is to define the scheduling of the work activities for each worker from a bi-objective perspective. The first objective is to reduce the ergonomic risk of repetitive work by varying the required movements and their intensity during the work shift. The second objective charges the workers with the activities that better fit their skills and abilities. Finally, the proposed model includes the ergonomic risk assessment of each scheduling solution, ensuring an acceptable exposure of the workers to the risk of repetitive work.

Keywords: Nonlinear system identification
Abstract: This paper studies parameter estimation of output error (OE) models. The commonly used approach of minimizing the free-run simulation error is called single shooting in contrast with the new multiple shooting approach proposed in this paper, for which the free-run simulation error of sub-datasets is minimized subject to equality constraints. The names "single shooting" and "multiple shooting" are used due to the similarities with techniques for estimating ODE (ordinary differential equation) parameters. Examples with nonlinear polynomial models illustrate the advantages of OE models as well as the capability of the multiple shooting approach to avoid undesirable local minima.

Keywords: Nonlinear system identification
Abstract: In this study, we propose an identification method of a state-space model for discrete-time SISO nonlinear systems. The first step of the procedure is to construct models which approximate local dynamics around stationary points of the system using a subspace method. The second step is to describe the local models in one unified coordinate using affine coordinate transformations. The coordinate transformations are determined based on the similarity of adjacent local models by the least-squares method. The model describes quasi-steady behavior of the plant. Simulation results of the proposed method are presented.

Keywords: Nonlinear system identification, Errors in variables identification, Time series modelling
Abstract: Inferring the exact topology of the interactions in a large, stochastic dynamical system from time-series data can often be prohibitive computationally and statistically without strong side information. One alternative is to seek approximations of the system topology that nonetheless describe the data well. In recent works, algorithms were proposed to identify sparse approximations which are optimal in terms of Kullback-Leibler divergence. Those algorithms relied on point estimates of statistics from the data. In this work, we investigate the more practical setting where point estimates are not reliable. We propose an algorithm to identify sparse, connected approximations that are robust to estimation error.

Keywords: Bayesian methods, Nonparametric methods
Abstract: This paper focuses on the kernel-based system identification methods, which estimate the impulse response of the target system in the Bayesian estimation framework. This paper discusses about continuous-time systems, and proposes a new kernel based on a prior that the relative degree of the target system is higher than or equal to two. Such a prior is identical to a prior on the continuity of the impulse response at time zero. The proposed kernel is an extension of the first-order Stable Spline kernel, which is one of the most famous kernels. Numerical examples are shown to demonstrate the effectiveness of the proposed kernel.

Keywords: Nonlinear system identification
Abstract: It is shown that ordinary fractional differential equations (FDEs) with the Riemann--Liouville fractional derivatives for the case when the order of fractional differentiation is close to an integer number can be reduced to differential equations of integer orders with a small parameter. Several examples illustrate that such equations with a small parameter have additional approximate symmetries in comparison with original FDEs and its open up new possibilities to construct approximate solutions of initial FDEs.

Keywords: Identification for control, Vibration and modal analysis, Time series modelling
Abstract: We present here some preliminary results on the problem of estimating pedestrian crowds from limited measurements. More specifically, we focus on a data-driven operator-based approach. We use the Koopman operator and its approximation with the kernel dynamic mode decomposition kDMD, to design a dynamical observer, which allows us to estimate the full crowd flow, based on a partial-view of a sensing camera. We explain the dynamical observer design, discuss its limitations, and propose some numerical simulations to validate the proposed approach.

Keywords: Nonlinear system identification, Bayesian methods
Abstract: In this paper we present some results on parametric identification for dynamical systems. More specifically, we consider the general case of dynamics described by partial differential equations (PDEs), which includes the special case of ordinary differential equations (ODEs). We follow a stochastic approach and formulate the identification problem as a Gaussian process optimization with respect to the unknown parameters of the PDE. We use proper orthogonal decomposition (POD) model reduction theory together with a data-driven Gaussian Process Upper Confidence Bound (GP-UCB), to solve the identification problem. The proposed approach is validated on the coupled Burgers' equation benchmark.

Keywords: Nonlinear system identification, Input and excitation design, Frequency domain identification
Abstract: Linearity is a common assumption for many real life systems. But in many cases, the nonlinear behavior of systems cannot be ignored and has to be modeled and estimated. Among the various classes of nonlinear models present in the literature, Parallel Hammertein Models (PHM) are interesting as they are at the same time easy to understand as well as to estimate when using exponential sine sweeps (ESS) based methods. However, the classical EES-based estimation procedure for PHM relies on a very specific input signal (ESS), which limits its use in practice. A method is proposed here based on the Welch method that allows for PHM estimation with arbitrary sine sweeps (ASS) which are a much broader class of input signals than ESS. Results show that for various ASS, the proposed method provides results that are in excellent agreement with the ones obtained with the classical ESS method.

Keywords: Nonlinear system identification, Nonparametric methods
Abstract: Hammerstein systems are composed by a static nonlinearity followed by a linear dynamic system. The proposed method for identifying Hammerstein systems consists of a formulation within the Least Squares Support Vector Machines (LS-SVM) framework where the Impulse Response of the system is incorporated as a constraint. A fundamental aspect of this work is that the structure of the Hammerstein system allows to obtain an impulse response that approximates the linear block while LSSVM models the nonlinearity. When the resulting model is trained, the regularization capabilities of LS-SVM are applied to the whole model. One of the main advantages of this method comes from the fact that while it incorporates information about the structure of the system, the solution of the model still follows from a simple linear system of equations. The performance of the proposed methodology is shown through two simulation examples and for different hyper-parameter tuning techniques.

Keywords: Nonlinear system identification, Recursive identification, Estimation and filtering
Abstract: A Hammerstein-Wiener model with time delay is a specific class of nonlinear time delay systems where the time delay which involves the system input and the parameters are unknown and need to be estimated using input-output data. The main difficulty that has been encountered in this identification problem is the additional nonlinearity due to the presence of the time delay in the criterion to be minimized. As a solution of this problem, an alternative approach is applied, which consists in estimating separately the unknown parameters and time delay. However, the conventional optimization techniques are not directly applicable. Hence, we formulate the problem of estimating the unknown time delay as a continuous relaxation problem and we then apply the gradient approach to estimate all unknown variables. Furthermore, by using the martingale convergence theorem, the convergence analysis of the proposed algorithm is treated. A numerical example is offered to demonstrate the effectiveness of the proposed method.

Keywords: Nonlinear system identification, Stochastic system identification, Particle filtering/Monte Carlo methods
Abstract: Nonlinear stochastic parametric models are widely used in various fields. However, for these models, the problem of maximum likelihood identification is very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the analytically intractable likelihood function and compute either the maximum likelihood or a Bayesian estimator. These methods, albeit asymptotically optimal, are computationally expensive. In this contribution, we present a simulation-based pseudo likelihood estimator for nonlinear stochastic models. It relies only on the first two moments of the model, which are easy to approximate using Monte-Carlo simulations on the model. The resulting estimator is consistent and asymptotically normal. We show that the pseudo maximum likelihood estimator, based on a multivariate normal family, solves a prediction error minimization problem using a parameterized norm and an implicit linear predictor. In the light of this interpretation, we compare with the predictor defined by an ensemble Kalman filter. Although not identical, simulations indicate a close relationship. The performance of the simulated pseudo maximum likelihood method is illustrated in three examples. They include a challenging state-space model of dimension 100 with one output and 2 unknown parameters, as well as an application-motivated model with 5 states, 2 outputs and 5 unknown parameters.

Keywords: Fault detection and diagnosis, Statistical data analysis
Abstract: Detecting changes in residuals is important for fault detection and is commonly performed by thresholding the residual using, for example, a CUSUM test. However, detecting variations in the residual distribution, not causing a change of bias or increased variance, is difficult using these methods. A plug-and-play residual change detection approach is proposed based on sequential quantile estimation to detect changes in the residual cumulative density function. An advantage of the proposed algorithm is that it is non-parametric and has low computational cost and memory usage which makes it suitable for on-line implementations where computational power is limited.

Keywords: Fault detection and diagnosis, Time series modelling
Abstract: Batch and semi-batch processes are often characterized with strong nonlinearity. Most of the existing kernel-based methods proposed for nonlinear batch process monitoring have not considered the nonlinear characteristic, sequential phase division and uneven problem simultaneously. In this article, a novel similarity index is first defined on the basis of the kernel technique to describe the similarity of nonlinear characteristic in the high feature space. Then, the pseudo time-slice is constructed for each sample by searching samples within a range resembling to each time using the k-nearest neighbor (kNN) rule, which can effectively tackle the uneven problem without trajectory synchronization. A novel automatic sequential phase division procedure is proposed by analysing the nonlinear similarity between the local models derived from the pseudo time-slice and the representative model of each phase. For online application, the affiliation of each new sample is real-time judged to determine the proper phase model and fault status can be distinguished from phase shift event. To illustrate the feasibility and effectiveness, the proposed algorithm is applied to fed-batch penicillin fermentation process.

Keywords: Nonlinear system identification
Abstract: The modeling of magnetorheological dampers combines the use of the laws of physics along with a phenomenological description. The phenomenological models that are used to describe these devices present a symmetric hysteresis loop. However, the experimental hysteresis loops of MR dampers are not symmetric. To take into account this asymmetry, we propose a modication of the viscous + Dahl model, use the modied model to describe a large-scale magnetorheological damper, and validate the modied model against experimental data.

Keywords: Errors in variables identification
Abstract: This work studies the problem of model order selection in errors-in-variables (EIV) system identification. Two criteria, the final correlated prediction error (FCPE) criterion and the final correlated output error (FCOE) criterion are developed for estimating the order of EIV systems. The criteria are extensions of the final prediction error (FPE) criterion and the final output error (FOE) criterion. To eliminate the influence of the input noise in FPE criterion and FOE criterion, the idea of the new criteria is to correlate the prediction error and the output error with the output signal respectively. Simulations are used to illustrate the performance of the criteria.

Keywords: Hybrid and distributed system identification, Bounded error identification
Abstract: In this paper, we address the problem of identification of Markovian jump ARX systems in the case where there is no a priori knowledge on the statistics of the process noise. The only assumptions made are the availability of known upper bounds on the subsystems order and noise level. The proposed method leverages available results to identify the subsystems models and provides a novel way to estimate the transition probability matrix of the switching in the presence of uncertainty. More precisely, since one cannot observe the exact switching sequence, the problem of estimating the transition probability matrix is formulated as a robust optimization problem, where one maximizes the minimum of the probability of all possible switching sequences. We show that this robust optimization problem can be efficiently solved by providing an equivalent convex formulation that can be solved using off-the-shelf software. Several academic examples are provided which illustrate the effectiveness of proposed approach.

Keywords: Nonlinear system identification, Time series modelling, Filtering and smoothing
Abstract: In empirical system identification, non-stationary structural disturbances, such as trends and outliers, can have a negative effect on the estimation of the system parameters. As it not possible to determine a priori which parts of the measured data stem from structural disturbances and which are due to the system dynamics, the identification of structural disturbances and system model should be done simultaneously. In this study, a method for output error identification of nonlinear Wiener models in the case when the measurement is affected by trends and outliers is presented. The Wiener model can be described by a dynamic linear block followed by an static nonlinear block. In the proposed method the dynamic block is expanded using orthonormal basis functions, while the static nonlinear block is modeled by a kernel model. The kernel parameters and structural disturbances are estimated simultaneously by using sparse optimization, which is solved using l1-regularization and iterative reweighting. The feasibility of the proposed method is demonstrated on a simulated example.

Keywords: Nonlinear system identification, Recursive identification, Filtering and smoothing
Abstract: Heston model is widely applied to financial institutions, while there still exist difficulties in estimating the parameters and volatilities of this model. In this paper, the pseudo-Maximum Likelihood Estimation and consistent extended Kalman filter algorithm (PMLE-CEKF) are implemented synchronously to estimate the Heston model . For parameter estimations, PMLE for the state equation and the measurement equation of the Heston model are conducted independently. For volatility estimations, a novel method, the consistent extended Kalman filter (CEKF) algorithm is introduced. The CEKF constructs an upper bound for the estimation covariance matrix error to ensure the algorithm to be well evaluated. Additionally, the estimation results of the Heston model are compared between PMLE-CEKF and PMLE-EKF algorithm. The numerical simulations illustrate that PMLE-CEKF algorithm works more efficiently than PMLE-EKF algorithm. Application of the PMLE-CEKF to S&P 500 shows the utility of the proposed algorithm.

Keywords: Recursive identification, Estimation and filtering, Filtering and smoothing
Abstract: While recent advances in online learning arising from the universal prediction perspective have led to algorithms with prediction performance guarantee, these techniques are not able to cope with high-dimensional data. This paper analyses a random projection gradient descent (RP-GD) algorithm which addresses this challenge and yields low theoretical regret bound and computationally efficient algorithm. It is shown that the performance of the algorithm converges to the performance of the best offline, computationally complex, high- dimensional algorithm in hindsight.

Keywords: Recursive identification, Hybrid and distributed system identification
Abstract: The problem considered in this paper is the identification from input/output data of Switched linear systems with Switched Output Error (SOE) models. The identification is carried out in the case where an upper bound on the noise is assumed to be known. This problem includes the estimation of the discrete state that corresponds to the active sub-model and the estimation of the parameters of all sub-models. The proposed algorithm is based on a modified Outer Bounding Ellipsoid (OBE) type algorithm suitable for identification with bounded noise. It proceeds in two successive stages. At each time, the first stage estimates the discrete state and in the second stage the parameter vectors are update. The stability and convergence proprieties are established.

Keywords: Software for system identification, Input and excitation design
Abstract: Optimization problems arising in Optimum Experimental Design (OED) applications require repeated covariance matrix evaluations of the underlying Parameter Estimation (PE) problems. The complexity of this task grows quickly with the problem dimensions, especially when process noise is considered. In this paper, a Schur complement method is proposed to alleviate this problem by translating the covariance matrix evaluation into the solution of a sparse linear system and the inversion of a small-scale matrix. The method is used as a building block for the open-source software package casiopeia, a powerful and easy-to-use environment for OED and PE. The performance of the software and the proposed Schur complement method is assessed on a numerical example.

Keywords: Nonlinear system identification, Time series modelling
Abstract: Over the past twenty years, exponential growth of the internet has led to a continuous struggle for content providers to maintain and improve their quality of service. Furthermore, the evolution of network architecture and increased inter-connectivity within the network has changed how traffic is communicated - and how we understand the internet as a whole.

Consequently, based on previous work by the authors, this paper formulates an Autonomous System (AS) level approach to traffic characterisation. Such an approach is advantageous given the current network topology, as traffic is overwhelmingly dominated by several agents - with each AS typically exhibiting its own unique, identifiable behaviour. Furthermore, two distinct modelling paradigms are proposed as ways of analysing and predicting network traffic data - from the time-series analysis and system identification communities respectively.

The applicability of the proposed modelling techniques is evaluated against real AS-level traffic data, obtained from the Tier-2 European Post Luxembourg network. Results show the suitability of the proposed approaches to the problem, and the simplicity of an AS-level approach from an analysis perspective.

Keywords: Traffic characterisation, autonomous systems, nonlinear system identification, time-series analysis.

Keywords: Nonparametric methods, Time series modelling, Identification for control
Abstract: In this paper we address the identification of (2D) spatial-temporal dynamical systems governed by the Vector-Auto-Regressive (VAR) form. The coefficient-matrices of the VAR model are parametrized as sums of Kronecker products. When the number of terms in the sum is small compared to the size of the matrix, such a Kronecker representation leads to high data compression. Estimating in least-squares sense the coefficient-matrices gives rise to a bilinear estimation problem, which is tackled using a three-stage algorithm. A numerical example demonstrates the advantages of the new modeling paradigm. It leads to comparable performances with the unstructured least-squares estimation of VAR models. However, the number of parameters in the new modeling paradigm grows linearly w.r.t. the number of nodes in the 2D sensor network instead of quadratically in the full unstructured matrix case.

Keywords: Time series modelling
Abstract: In a context of sustainable development, interest for Concentrating Solar Power (CSP) is growing rapidly. One of the most challenging topics is to improve solar resource assessment and forecasting in order to optimize power plant operation. Indeed, in CSP plants, electricity generation is directly impacted by both availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). Moreover, in the framework of the CSPIMP research project, PROMES-CNRS has developped a sky imager able to provide High Dymanic Range (HDR) images. As a result, the present paper deals with the short- term forecasting of DNI using sky-imaging data. Preliminary results highlight that models (in particular based on artificial intelligence tools) that make use of the fractional cloud cover have the potential to outperform persistence models in terms of forecasting accuracy.

Keywords: Time series modelling, Errors in variables identification
Abstract: This paper proposes a new method for identifying ARMA models in the presence of additive white noise. The method operates with two main steps. First, the noisy ARMA model is approximated by the sum of an high-order AR model with an additive white noise. The parameters of the high-order AR model as well as the driving noise and the additive noise variances are estimated by using an errors-in- variables approach. Second, the coefficients of the ARMA model are extracted from those of the AR model previously identified by means of existing techniques. In particular, three different methods are considered and compared for solving the second step. The effectiveness of the described identification procedure has been tested by Monte Carlo simulations and compared with a prediction error method.

Keywords: Time series modelling, Estimation and filtering, Software for system identification
Abstract: Fitting a signal to a sum-of-exponentials model is a basic problem in signal processing. It can be posed and solved as a Hankel structured low-rank matrix approximation problem. Subsequently, local optimization, subspace, and convex relaxation methods can be used for the numerical solution. In this paper, we show another approach, based on the recently developed concept of structured data fusion. Structured data fusion problems are solved in the Tensorlab toolbox by local optimization methods. The approach allows fitting of signals with missing samples and adding constraints on the model, such as fixed exponents and common dynamics in multi-channel estimation problems. These problems are non-trivial to solve by other existing methods. Tensorlab is publicly available and the results presented are reproducible.

Keywords: Multi-agent systems, Complex system management, Sensor networks
Abstract: In this work, we address the problem of identifying a set of nodes that are critical to the rate of convergence of consensus dynamics in large-scale spatial networks. By assuming that nodes are uniformly distributed over a spatial region and that these can communicate with others in an infinitesimally small neighborhood, we start by formulating the consensus problem by means of a partial differential equation involving the Laplace operator, subject to Neumann boundary condition. As with its finite-dimensional counterpart, we observe how the performance of these dynamics is directly related to the second smallest eigenvalue of the Laplace operator over the domain of interest. We then reduce the critical node set identification problem to that of finding a ball of fixed radius, whose removal minimizes the rate of convergence over the residual domain. This leads us to consider two functional optimization problems. First, we treat the problem of determining the second smallest eigenvalue for a fixed domain by minimizing an energy functional. We characterize the critical points of the energy functional, and then construct the gradient dynamics that converge to the set of critical points. We then prove that the only locally asymptotically stable critical point is the second eigenfunction of the Laplace operator. Building on these results, we consider the critical ball identification problem, provide a characterization of the critical points, and define gradient dynamics to converge asymptotically to these points.

Keywords: Bayesian methods, Estimation and filtering
Abstract: This paper proposes a hyper-parameter estimation algorithm for the regularized least squares problem in the empirical Bayesian approach arising from FIR model identification using OBFs (orthonormal basis functions)-based kernels. The algorithm consists of two steps by dividing the decision variables into two groups and alternately minimizing wit respect to each group. It is shown that DC (difference of convex functions) programming is effectively applicable in the algorithm because the search space is shown to be bounded. The paper includes a couple of numerical simulations to show the efficiency of the method.

Keywords: Control of systems in vehicles
Abstract: Anti-skid control systems are standard equipment for aircraft of all sizes. Such systems are designed by rather few braking systems suppliers, with control algorithms that have never been presented, nor analysed, in the scientific literature. From industrial practice, it is known that such controllers act on a single landing gear, that they use a single wheel speed measure, and that they require extensive trial-and-error tuning before release. In this paper, we first analyse experimental data measured on closed-loop braking tests to see which is the behaviour of the controlled aircraft when braking. Then, we propose a control-oriented modelling of the landing gear, that allow disclosing its dynamic features. Based on this, we present an anti-skid control algorithm that achieves a very similar closed-loop behaviour to that experimentally experienced. The performance of the controller are quantitatively analysed according to cost functions that are standards in the aeronautic braking systems evaluation.

Keywords: Health monitoring and diagnosis, Guidance, navigation and control of vehicles, Avionics and on-board equipments
Abstract: This paper addresses the estimation of flight parameters in nominal and degraded flight conditions when one or several specific sensors become faulty. Reconstructing through time the missing signals is a challenging objective for Flight Control Systems (FCS), especially because some of them are essential for control purposes. To this end, this paper proposes a solution based on virtual sensors which makes use of an Adaptive Extended Kalman Filter (AEKF). This allows the longitudinal flight parameters of a civil aircraft to be estimated on-line after the occurrence of one or several failures. This solution offers interesting monitoring capabilities including the detection of common mode failures (i.e., simultaneous failures occurring on redundant sensors), and also provides the capability of correcting a wrong aircraft weight in addition to the sensors' monitoring. Typical results using real flight test data are shown to evaluate the performances of the approach in terms of detection and estimation capabilities.

Keywords: Guidance, navigation and control of vehicles, Control of systems in vehicles, Avionics and on-board equipments
Abstract: Control architecture sizing is a main challenge for new aircraft such as blended wing-body design. This aircraft configuration typically features redundant elevons located at the trailing edge of the wing, acting simultaneously on pitch and roll axes. The problem of integrated design of control surface sizes and flight control laws for an unstable blended wing-body aircraft is addressed here. Latest tools for H_infty non-smooth optimization of structured controllers are used to optimize in a single step the gains for both longitudinal and lateral control laws, and a control allocation module, while minimizing control surfaces total span. Following constraints are ensured: maximal deflection angles and rates for 1) pilot longitudinal pull-up 2) pilot bank angle order and 3) longitudinal turbulence. Using this coupled approach, significant gains in terms of outer elevons span compared to the initial layout are demonstrated, while closed-loop handling qualities constraints are guaranteed.

Keywords: Guidance, navigation and control of vehicles, Autonomous systems
Abstract: This paper demonstrates flight test results of disturbance attenuation of an aircraft by preview control, which was explained and its hardware-in-the-loop (HIL) simulation results were reported in a previous paper. The design methodology of preview control used here is the ``two-stage'' approach, in which a feedback gain is firstly designed by some standard design method and then a preview feedforward compensation is designed for the closed loop system. The flight test is carried out using a small research aircraft and a preview controller that alleviates disturbance response is designed for the aircraft model. Flight test results shows that the proposed preview controller alleviates vertical acceleration much better compared to the conventional feedback controller.

Keywords: Control of systems in vehicles
Abstract: Bleed-air systems in passenger aircraft are often prone to limit cycle oscillations. Tuning of such systems makes additional flight-tests necessary, generating large expenses. In this paper, several control approaches to improve the performance of bleed-air systems are compared. These approaches combine a base-controller with stiction-compensation techniques. The different approaches are implemented, optimized and evaluated using a high-fidelity bleed-air system model, described in the equation-based modelling language Modelica. Results indicate that interaction between different valves should be reduced as much as possible. If a suitable base control approach is chosen, the influence of a superimposed stiction compensation technique seems to be small.

Keywords: Autonomous systems, Guidance, navigation and control of vehicles, Cooperative navigation
Abstract: An aircraft defense differential game with non-zero capture radius is analyzed. A Target aircraft and a Defender missile team up to defeat an Attacker missile. The Attacker pursues the (slower) Target which evades the Attacker with the help of the Defender, which is endowed with a non-zero capture radius. The Target and the Defender cooperate in a way that the Defender intercepts the Attacker before the latter reaches the Target. The payoff functional is the distance between the Target and the Attacker at the interception time which the Attacker strives to minimize and the Target/Defender team strives to maximize. The work in this paper generalizes existing results to consider a non-zero capture radius as opposed to the limiting case of point capture.

Keywords: Multiagent systems, Methodologies and tools for analysis of complexity, Hierarchical multilevel and multilayer control
Abstract: This paper presents a characterization of the algebraic connectivity of a multi-agent system (MAS) with hierarchical network structure having heterogeneous subsystems. The MAS is represented as an interconnected system of several kinds of multi-agent subsystems via a certain structure, where several vertices of each subsystem are connected to the structure, that is, to other subsystems. The algebraic connectivity of this MAS can be computed by using a corresponding MAS having homogeneous subsystems under a certain condition, which is investigated in this paper.

Keywords: Efficient strategies for large scale complex systems, Multiagent systems, Optimization and control of large-scale network systems
Abstract: An asynchronous gossip algorithm with a restart strategy is proposed to an approximated distributed minimax optimization in this paper. The restart strategy controls a step length of a subgradient method and resets a local clock under a certain criteria in the algorithm. The convergence property is analyzed for the algorithm with the restart strategy. Numerical examples illustrate that the proposed algorithm works well in various instances and high approximation ratios.

Keywords: Modelling and decision making in complex systems, Intelligent decision support systems in manufacturing, Methodologies and tools for analysis of complexity
Abstract: How to make, early in the development cycle of complex products, the most promising design choices as regards the customer’s requirements and being at the limit of what is technically feasible by the manufacturer? To contribute to solve such a difficult problematic, we propose an original approach based on possibility theory that aims finding the best alternative according to the preferences of the stakeholders and being feasible by the designer team. Customer’s and manufacturer’s preference are captured in a multi attribute utility theory (MAUT) framework that is extended to uncertain and imprecise evaluation of the alternatives’ characteristics since available knowledge about the future system is mostly qualitative in preliminary design stage.

Keywords: Intelligent system techniques and applications, Complex logistic systems, Intelligent decision support systems in manufacturing
Abstract: Non-stop growth of world economies effects in the increasing of the complexity and technological level of integration international supply chains. Both the amount of daily decisions in supply chains and the number of involved in these processes decision makers grows continuously. Electronic seal (E-seal) is an intelligent technology in container logistics that has unrealised potential for different business logistics smart applications. This paper discusses the advantages of E-seals to automate the decision making processes in international supply chains. Proposed discussion is based on already realized business projects and theoretical researches about different types of electronic seals.

Keywords: Modelling and decision making in complex systems, Efficient strategies for large scale complex systems, Maintenance models and services
Abstract: This paper proposes a new method of fault diagnosis of cyber-physical systems (CPS). The complexity of these systems in heterogeneous technologies require elaborate diagnostic techniques and maintenance. We illustrate our methodology on a telepresence robot "RobAIR" as an example of CPS. First, we applied a knowledge modeling approach on this case study through the representation of four types of knowledge: functional, structural, topological and behavioral. These representations are achieved using a modeling tool "Papyrus". Secondly, from knowledge representation, three algorithms for detection and fault identification are proposed to remove the ambiguity in functions and components. These algorithms will be explained through the RobAIR example.

Keywords: Multi-agent systems applied to industrial systems, Modeling of manufacturing operations, Modeling of assembly units
Abstract: During the last eighty years, new work philosophies has been introduced and technological advancement changed radically the way of work, making it more reactive, agile but complex as well. As a result, classical approaches for production system design may no longer be sufficient to ensure productivity and safety of industrial systems. In the domain of occupational diseases, adopting a pure biomechanical approach, consisting in ensuring the non-violation of workers’ biomechanical limits at each workstation is proved to be uncomplete. Beside biomechanical risk factors, psychosocial risk factors, which are strongly linked to the dynamic of the physical and informational flows, may contribute to the genesis of Musculoskeletal Disorders. By granting a certain workers’ margins of manoeuver, these risk factors can be limited. This article introduces AEN-PRO, a simulation tool for investigating the impact of physical flow on the production system and particularly workers, to assess their margins of manoeuver and to ensure safer and productive systems.

Keywords: Fault detection and diagnosis
Abstract: In this paper, an analytical method issued from the field of reliability analysis is used for prognosis. The inverse first-order reliability method (Inverse FORM) is an uncertainty propagation method that can be adapted to remaining useful life (RUL) calculation. An extended Kalman filter (EKF) is first applied to estimate the current degradation state of the system, then the Inverse FORM allows to compute the probability density function (pdf) of the RUL. In the proposed Inverse FORM methodology, an analytical or numerical solution to the differential equation that describes the evolution of the system degradation is required to calculate the RUL model. In this work, the method is applied to a Paris fatigue crack growth model, and then compared to filter-based methods such as EKF and particle filter using performance evaluation metrics (precision, accuracy and timeliness). The main advantage of the Inverse FORM is its ability to compute the pdf of the RUL at a lower computational cost.

Keywords: Fault detection and diagnosis
Abstract: This paper is dedicated to the problem of fault tolerant control for Lipshitz nonlinear system. As longitudinal vehicle dynamic is nonlinear, it is greatest to deal with the nonlinearities in order to ensure stability and even reliability. A fault detection and estimation observer is designed in this work to achieve system performances in event of sensor faults. An active fault tolerant control based on state feedback with an integral action control scheme is designed in order to ensure the stability robustness under fault occurrence. Indeed both designed observers and controller gains are designed by solving the LMI problem in such a way that the system remains robustly insensitive to external disturbances and sensor fault. Simulation results are addressed to validate the eectivness of the porposed controller in dierent autonomous vehicle driving scenarios.

Keywords: Fault detection and diagnosis
Abstract: Distributed diagnosis is important for complex systems as a way to reduce computational costs or for large systems that require minimizing data transfer. This paper presents a distributed diagnosis method for continuous systems that only requires the knowledge of local models and limited knowledge of their neighboring subsystems. The notion of Fault Driven Minimal Structurally Overdetermined (FMSO) set is used as the corner stone of the design of residual generators. We show that all the FMSO sets of the global system can be obtained in a distributed manner from so-called shared FMSO sets and shared CMSO sets that are computed along a structural approach for every local site.

Keywords: Fault detection and diagnosis
Abstract: The paper proposes an effective change detection test for online monitoring data streams by inspecting the least squares density difference (LSDD) features extracted from two non-overlapped windows. The first window contains samples associated with the pre-change probability distribution function (pdf) and the second one with the post-change one (that differs from the former if a change in stationarity occurs). This method can detect changes by also controlling the false positive rate. However, since the window sizes is fixed after the test has been configured (it has to be small to reduce the execution time), the method may fail to detect changes with small magnitude which need more samples to reach the requested level of confidence. In this paper, we extend our work to the Big Data framework by applying the Kolmogorov-Smirnov test (KS test) to infer changes. Experiments show that the proposed method is effective in detecting changes.

Keywords: Fault detection and diagnosis, Adaptive control -applications, Robust adaptive control
Abstract: In this paper, an adaptive failure compensation is proposed for high-speed trains with traction system actuator failures to achieve the position tracking. To deal with the time-varying parameters of the train motion dynamics, the piecewise constant model is introduced to describe the train dynamics with variable parameters. For the system with actuator failures, the adaptive controller with the adaptive laws is designed to achieve the position tracking, in the presence of the system piecewise constant parameters and actuator failure parameters which are unknown. Simulation results on a high-speed train model are presented to illustrate the performance of the developed adaptive actuator failure compensation control scheme.

Keywords: Fault detection and diagnosis, Adaptive system and control
Abstract: An adaptive Kalman filter is proposed in this paper for actuator fault diagnosis in discrete time stochastic time varying systems. By modeling actuator faults as parameter changes, fault diagnosis is performed through joint state-parameter estimation in the considered stochastic framework. Under the classical uniform complete observability-controllability conditions and a persistent excitation condition, the exponential stability of the proposed adaptive Kalman filter is rigorously analyzed. The minimum variance property of the combined state and parameter estimation errors is also demonstrated. Numerical examples are presented to illustrate the performance of the proposed algorithm.

Keywords: Distributed control and estimation, Multi-agent systems, Control over networks
Abstract: It is well known that a system is stabilizable if and only if its unstable part is controllable. However, constraints placed on the system's controller - such as those imposed by a distributed system's interconnection structure - can render this condition insufficient. In this paper, we provide a new necessary and sufficient condition for the stabilizability of distributed systems encoded by some pattern that is reflected in the system matrices. We also provide sufficient conditions for stabilizability by static measurement feedback in these systems. Our conditions are generalizations of those from the standard geometric approach.

Keywords: Distributed control and estimation, Multi-agent systems, Estimation and filtering
Abstract: In this paper, we consider the problem of distributed sensor fusion in relay-assisted sensor networks with time-varying asymmetric communications over fading channels. A Distributed INnovation based Scheme (DINS) is proposed by fusing both the local measurements and the information received from neighboring nodes. We present a hybrid TDMA scheme for efficient implementation of DINS and the design of parameters of DINS to bring the effects of fading under a satisfactory level. With an appropriate design, it is shown that our algorithm is able to produce estimates with desired asymptotic properties of unbiasedness and consistency. Simulation results are provided to demonstrate the performance of DINS.

Keywords: Control of networks, Control in system biology, Multi-agent systems
Abstract: A Boolean network is a discrete-time finite state dynamical system, whose variables take values from the binary set {0,1}, and the value update rules are Boolean functions. A conjunctive Boolean network is a special type of Boolean network, whose value update rule for each variable is comprised only of "AND" operations. Recently there have been extensive investigations on conjunctive Boolean networks. Questions about asymptotic behaviors, stabilities of periodic orbits, and reachability and observability have all been addressed to some extent. We focus in this paper on controllability of a conjunctive Boolean network. Specifically, assuming that there is a selected subset of variables whose values are determined by external control inputs, we pose and answer the question of whether (and how) one can steer the system from any initial state to any final state. We establish a necessary and sufficient condition, via a graphical approach, for a conjunctive Boolean network to be controllable. An explicit control law is also presented along the analysis.

Keywords: Distributed control and estimation, Cooperative systems, Multi-agent systems
Abstract: We present a framework for distributed control where the subsystems estimate overlapping components of the state of the overall system. This enables the implementation of decentralized state feedback controllers, which depend on the overlapping state estimates. For a distributed framework, communication can be added. By chosing the amount of communication and the degree of overlap in the state estimates, a trade-off between increasing computational effort, communication, and performance can be made. This approach is especially suited for systems with strongly coupled subsystems, which are restricted in communication during their operation. The computational effort of each subsystem depends only on the chosen degree of overlap in the estimates and thus stays constant with an increasing number of subsystems, which makes the approach convenient for the operation of large systems. The design problem of the distributed control and estimation is formulated with bilinear matrix inequalities in an augmented state space. A numerical example of a cooperative manipulation task illustrates the performance of the distributed control and estimation scheme.

Keywords: Control of networks, Multi-agent systems, Control over networks
Abstract: In this paper we show how average consensus can be guaranteed in cooperative control schemes for multi-agent systems, even when normalised or weighted Laplacians are used. First we present weighting strategies for improving the performance that can be applied in a distributed manner (only local information is used), based on a recently proposed importance matrix. Then we modify the first-order consensus protocol, such that average consensus is guaranteed. It is then shown how this scheme can be used to improve the performance of cooperative control schemes. For this purpose, the effect of a formation reference on the consensus states is explicitly stated. A simulation result with second order LTI agent models illustrates the proposed approach.

Keywords: Distributed control and estimation, Estimation and filtering, Sensor networks
Abstract: In this paper, the distributed state estimation problem of a sensor network is studied with event-based communication scheme. A novel scalable event-based distributed Kalman filtering algorithm is proposed to reduce the burden of communications between sensors. A deterministic triggering mechanism depending on a lower bound of the true information matrix is developed. Also, the stability conclusion of the distributed filter is provided under certain conditions of collective observability and suitable connectivity. The numerical simulation illustrates the feasibility as well as the effectiveness of the proposed algorithm.

Keywords: Distributed robust controller synthesis, Decentralized control, Robust control applications
Abstract: Recent studies in the literature have shown that cooperative energy management of an aggregation of buildings may lead to substantial energy savings. These approaches typically assume the existence of a central operator that is capable of formulating and solving, within a reasonable amount of time, a centralized optimization problem. However, this requirement may be unrealizable in cases of large scale districts, and it also fails to address privacy concerns of the building occupants. In this paper, we deal with these issues by proposing a decentralized control scheme which only requires the individual buildings to communicate bounds on their energy demands. The proposed method partly alleviates concerns on privacy since this limited communication scheme does not reveal the exact characteristics of the energy usage within each building. In addition, it enables a distributed computation of the solution, making our method highly scalable. We demonstrate through a numerical study the efficacy of the proposed approach, which leads to solutions that closely approximate those obtained by the centralized formulation only at a fraction of the computational effort.

Keywords: Large scale optimization problems, Differential or dynamic games, Energy systems
Abstract: We consider the problem of optimal charging of heterogeneous plug-in electric vehicles (PEVs). We approach the problem as a multi-agent game in the presence of constraints and formulate an auxiliary minimization program whose solution is shown to be the unique Nash equilibrium of the PEV charging control game, for any finite number of possibly heterogeneous agents. Assuming that the parameters defining the constraints of each vehicle are drawn randomly from a given distribution, we show that, as the number of agents tends to infinity, the value of the game achieved by the Nash equilibrium and the social optimum of the cooperative counterpart of the problem under study coincide for almost any choice of the random heterogeneity parameters. To the best of our knowledge, this result quantifies for the first time the asymptotic behaviour of the price of anarchy for this class of games. A numerical investigation to support our result is also provided.

Keywords: Large scale optimization problems, Networked systems, Convex optimization
Abstract: In this paper we consider a distributed optimization scenario in which a set of agents has to solve a convex optimization problem with separable cost function, local constraint sets and a coupling inequality constraint. We propose a novel distributed algorithm based on a relaxation of the primal problem and an elegant exploration of duality theory. Despite its complex derivation based on several duality steps, the distributed algorithm has a very simple and intuitive structure. That is, each node solves a local version of the original problem relaxation, and updates suitable dual variables. We prove the algorithm correctness and show its effectiveness via numerical computations.

Keywords: Networked systems, Differential or dynamic games, Large scale optimization problems
Abstract: Networked control systems must use communication links between control hubs and distributed components, possibly both to observe component states, and to send control commands. We consider a model of a CDMA based communication and control system where the power sent from the components to the base station, acting as the control hub, is proportional to their (scalar) state, and in turn, the base station sends back the required control commands to the components. The systems are linear, and commands are constrained to be linear, possibly time varying feedback laws on current and a limited set of recent measurements. However, the individual measurements as decoded by the base station include interference terms from the set of all other components, and this inadvertently creates an interference induced game situation. The consequence is that controls have dual effects: they steer individual systems, but they can also help create additional interference. We propose an algorithm which accounts for a combination of control and estimation costs to compute symmetric Nash equilibria if they exist.

Keywords: Large scale optimization problems, Differential or dynamic games, Convex optimization
Abstract: Large-scale multi-agent systems arise in several control applications, such as demand side management in smart power networks, traffic control in intelligent transportation systems, and congestion control in communication networks. All these systems comprise a large population of noncooperative agents whose decision making process can be modeled as a generalized aggregative game. In this paper, we analyze the usual convexity assumption and the monotonicity of the so-called game mapping. Specically, we show necessary and sufficient conditions on the problem data such that monotonicity holds. The derived conditions show that monotonicity cannot be expected in general.

Keywords: Complex systems, Networked systems
Abstract: In a prior work, we investigated how the outcome of a tracking poll can be influenced by two contradictory behaviors of participants, namely conformity and manipulation. A common characteristic of opinion polls is that a participant has only a few options to express her opinion, thus such polls do not capture the opinion dynamics of social networks where the stated opinions are deemed to belong a continuum. Inspired by this argument, we address in this paper the conformist and manipulative behaviors of individuals in a realistic opinion network. Such an opinion network can indeed be viewed as a tracking poll where a participant casts her vote by selecting from a continuum rather than a discrete set. To carry out the analysis, we model an opinion network as a game among the individuals and define their payoff functions appropriately in the sense that optimizing them captures the conformist and manipulative behaviors. We then investigate the dynamics of the game and its behavior as time grows.

Keywords: Delay systems, Asymptotic stabilization, Digital implementation
Abstract: The paper deals with the stabilization of nonlinear sampled-data systems affected by non-entire input-delays. The approach combines input-Lyapunov matching, prediction and Immersion and Invariance concepts. The result is also detailed for linear dynamics and illustrated on a benchmark example.

Keywords: Delay systems, Input-to-State Stability, Lyapunov methods
Abstract: In this paper, we address the question whether input-to-state stability (ISS) of nonlinear time-delay systems is guaranteed when a Lyapunov-Krasovskii functional (LKF) satisfies a dissipation inequality in which the dissipation rate involves solely the present value of the state. We do not yet confirm or infirm this conjecture, but rather identify growth restrictions, on the LKF or on the vector field ruling the dynamics, under which it holds true. An example taken from the neuroscience literature illustrates our findings. We also list a series of robustness properties that naturally hold under this point-wise dissipation inequality, and indicate possible research directions to confirm our conjecture.

Keywords: Delay systems, Lyapunov methods, Stability of nonlinear systems
Abstract: This work concerns with several notions on output stability in the framework of input-to-state stability for systems affected by time-delays. These notions extend the notion of input-to-state stability to the case when output variables, not necessarily the full set of state variables, are considered for stability properties. The main results provide equivalent Lyapunov-Krasovskii characterizations for the input-to-output stability property.

Keywords: Delay systems, Lyapunov methods, Telerobotics
Abstract: Bilateral teleoperators allow people to perform complex tasks in a remote or inaccessible environment. Since the local and remote manipulators are connected via a communication channel there may be substantial delays in data transmission, which does not allow neither exact tracking position nor transparency. For this reason, most control approaches are meant to guarantee only position regulation, when a human interacts with the local robot, or to synchronize the local and the remote manipulator by inducing periodic position trajectories. Another problem that may be present is the lack of velocities measurements. In this work, a teleoperation control--observer scheme is proposed for bilateral systems with variable delays. It is shown that the observation errors tend to zero while position consensus or synchronization is achieved in free motion. Simulation results are provided to validate the proposed algorithm.

Keywords: Control of switched systems, Lyapunov methods, Adaptive control
Abstract: In this paper, we develop a robust controller to accomplish a herding task, i.e., using a directly controllable agent to influence the states of target agents which are not directly controllable. This paper employs constructive control design strategies and switched systems analysis to control a herding agent in a way that regulates fleeing target agents to a goal location. Lyapunov-based analysis is used to guarantee exponential regulation of the target agents. Preliminary simulation results are included to illustrate the performance of the developed controller.

Keywords: Control of switched systems, Sum-of-squares, Convex optimization
Abstract: In this paper we consider the problem of designing an observer-based controller for switched linear system under arbitrary switching, with guaranteed l-infinity to l-infinity performance. It is shown that, by using the concepts of polyhedral Lyapunov functions and superstability, the problem can be recast into a nonconvex constrained Polynomial Optimization Problem (POP). In turn, this problem can be relaxed into a sequence of convex Semi-Definite Programs (SDPs) by resorting to moments-based polynomial optimization techniques. Further, by exploiting the sparsity pattern exhibited by the problem, we show that the original POP can be equivalently reformulated as a SDP problem with a rank-1 constraint. An efficient algorithm is proposed to solve the later, by seeking low rank solution through a re-weighted nuclear norm minimization.

Keywords: Real-time control, Control of constrained systems, Controller constraints and structure
Abstract: This paper presents SPLIT, a C-code generation tool for Model Predictive Control (MPC) based on operator splitting methods. In contrast to existing code generation packages, SPLIT is capable of generating both software and hardware-oriented C-code to allow quick prototyping of optimization algorithms on conventional CPUs and eld-programmable gate arrays (FPGAs). A Matlab interface is provided for compatibility with existing commercial and open-source software packages. A numerical study compares software, hardware and heterogeneous implementations of splitting methods and investigates MPC design trade-offs. For the considered testcases the reported speedup of hardware implementations over software realizations is 3x to 11x.

Keywords: Nonlinear predictive control
Abstract: Nonlinear Model Predictive Control (NMPC) requires the efficient treatment of the dynamic model in the form of a system of continuous-time differential equations. Newton-type optimization relies on a numerical simulation method in addition to the propagation of first or higher order derivatives. In the case of stiff or implicitly defined dynamics, implicit integration schemes are typically preferred. This paper proposes a tailored implementation of the necessary linear algebra routines (LU factorization and triangular solutions), in order to allow for a considerable computational speedup of such integrators. In particular, the open-source BLASFEO framework is presented as a library of efficient linear algebra routines for small to medium-scale embedded optimization applications. Its performance is illustrated on the nonlinear optimal control example of a chain of masses. The proposed library allows for considerable speedups and it is found to be overall competitive with both a code-generated solver and a high-performance BLAS implementation.

Keywords: Robust control (linear case), Real-time control
Abstract: Robust multi-stage Model Predictive Control (MPC) is an increasingly popular approach to handle model uncertainties due to the simplicity of its problem formulation and other attractive properties. However, the exponential growth of the problem dimensions with respect to the robust horizon renders the online solution of such problems challenging and the development of tailored solvers crucial. In this paper, an interior point method is presented that can solve Quadratic Programs (QPs) arising in multi-stage MPC efficiently by means of a tree-structured Riccati recursion and a high-performance linear algebra library. A performance comparison with code-generated and general purpose sparse QP solvers shows that the computation times can be significantly reduced for all problem sizes that are practically relevant in embedded MPC applications. The presented implementation is freely available as part of the open-source software HPMPC.

Keywords: Systems with time-delays, Optimal control theory
Abstract: In this paper a generic Model Predictive Control design procedure is studied with a specific attention to linear discrete time-delay models and dynamics affected by input/state constraints. The starting point in the analysis is the design of a local stabilizing control law using different feedback structures. In order to ensure stability and guarantee input and state constraints satisfaction of the moving horizon controller, the concept of positive invariance related to time-delay systems is investigated. Using the ”terminal set-terminal cost” design, the states are forced to attain the maximal delayed-state admissible set at the end of the prediction horizon. We show that λ-D-contractive sets can be used instead as a terminal region in which the present and the delayed states are forced to lie in a finite horizon.

Keywords: Real-time control, Constrained control, Industrial applications of optimal control
Abstract: Programmable logic controllers (PLCs) have the benefit of being able to withstand a variety of environments while maintaining high reliability compared to computers and other controllers. Traditionally, PLCs have been used for simple ON-OFF control schemes or for proportional-integral-derivative (PID) control. However, recent developments in technology have allowed even low-cost PLCs to implement more advanced and efficient algorithms. This paper investigates a PLC implementation of the specialized Projected Gauss-Seidel (PGS) algorithm. The PGS algorithm is able to quickly and efficiently solve both symmetric and asymmetric mathematical programming problems. The algorithm can be further utilized to solve more complicated problems such as model predictive control (MPC). The focus of this paper is to solve a general constrained optimization problem on a PLC and to expand the capability to solve a MPC example. The DirectLogic Do-More PLC is exploited both for computational efficiency and for memory utilization.

Keywords: Control of constrained systems, Large scale optimization problems, Real-time control
Abstract: In this paper, we propose an iterative first-order gradient method for fast and efficient implementation of Model Predictive Control (MPC). The proposed method utilizes a matrix-splitting scheme common to successive over-relaxation (SOR) iterative methods and may be considered a generalization of many existing fast gradient-based methods for MPC. We comment on parameter choices for global convergence and demonstrate the efficiency of the ensuing algorithms using a simulation example.

Keywords: Cooperative systems, Multi-agent systems, Distributed control and estimation
Abstract: In this paper, we consider cooperative multi-agent systems minimizing a social cost. Cooperation is induced by the cost that penalizes the divergence of each agent from the average collective behavior. In principle, the optimal solution is centralized and requires a complete communication graph. We study this problem for two important system input-output norms, the l1 induced norm and the per-agent H2 norm squared, as function of the number of agents, n, and various cost structures. For the case of identical agents, and the simplest cost setting, we show that the optimal social solution is always the optimal decentralized selfish solution. For more general cost functions, we show that the optimal solution is always decentralized in the l1 induced norm case. In the case of the per-agent H2 norm squared cost, we show that the optimal decentralized selfish solution is socially optimal in the limit of large n. All of these also hold for several classes of problems with nonidentical agents. In simple terms, these results, identify important problem classes where decentralized/selfish behaviors are socially optimal, and for which inter-agent communication is or becomes unnecessary for large n.

Keywords: Control over networks, Estimation and filtering
Abstract: This paper considers the problem of sensory data scheduling of multiple processes. There are n independent linear time-invariant processes and a remote estimator monitoring all the processes. Each process is measured by a sensor, which sends its local state estimate to the remote estimator. The sizes of the packets are different due to different dimensions of each process, and thus it may take different lengths of time steps for the sensors to send their data. Because of bandwidth limitation, only a portion of all the sensors are allowed to transmit in each time step. Our goal is to minimize the average of estimation error covariance of the whole system at the remote estimator. The problem is formulated as a Markov decision process (MDP) with average cost over an infinite time horizon. We prove the existence of a deterministic and stationary policy for the problem. We also find that the optimal policy has a consistent behavior and threshold type structure. A numerical example is provided to illustrate our main results.

Keywords: Multi-agent systems, Distributed control and estimation, Cooperative systems
Abstract: This paper is concerned with a constrained optimization problem over a directed graph (digraph) of nodes, in which the cost function is a sum of local objectives, and each node only knows its local objective and constraints. To collaboratively solve the optimization, most of the existing works require the interaction graph to be balanced or ``doubly-stochastic'', which is quite restrictive and not necessary as shown in this paper. We focus on an epigraph form of the original optimization to resolve the ``unbalanced'' problem, and design a novel two-step recursive algorithm with a simple structure. Under strongly connected digraphs, we prove that each node asymptotically converges to some common optimal solution. Finally, simulations are performed to illustrate the effectiveness of the proposed algorithms.

Keywords: Multi-agent systems
Abstract: In this paper, we deal with simplification/aggregation of large-scaled optimization problems. Motivated by the reduction of data transmission and computation in distributed optimization algorithms and necessity of a criterion for clustering agents in multi-agent systems such as electric power networks, we clarify the relationship between optimal solutions of original large-scaled optimization problems and the corresponding approximated optimization problems. Based on this analysis, we propose a hierarchically distributed optimization algorithm which reduces the data transmission and computation considerably. Furthermore, we assume stochastic dispersion on profit functions of agents and analyze the stochastic characteristics of the solutions of the optimization problems.

Keywords: Control of networks
Abstract: Structural control and influence maximization on networks both admit the problem of selecting a particular subset of nodes. In structural control, the subset of nodes should guarantee the controllability of the network (in the usual sense) for almost any combination of weights. In influence maximization, given a diffusion process over the network, the chosen subset of nodes (of a given cardinality) should produce the greatest diffusive influence over the rest of the network. While structural control exploits only the structure of the network, influence maximization depends both on the structure and the weights of the edges. We modify an algorithm originally developed for structural control to take advantage of the weights as well, and we show it can be used to find competitive solutions to the influence maximization problem, while guaranteeing structural controllability. This also suggests an underlying similarity between these models, despite their intrinsic differences and the contexts in which they are usually used. We develop analytic results for two extreme cases, the binary tree and the two-level star graph, as well as empirical results for a selection of random graphs.

Keywords: Control under communication constraints, Switching control, Event-based control
Abstract: In this paper, we consider hands-o control via minimization of the CLOT (Combined L-One and Two) norm. The maximum hands-o control is the L0-optimal (or the sparsest) control among all feasible controls that are bounded by a specified value and transfer the state from a given initial state to the origin within a fixed time duration. In general, the maximum hands-o control is a bang-o-bang control taking values of 1 and 0. For many real applications, such discontinuity in the control is not desirable. To obtain a continuous but still relatively sparse control, we propose to use the CLOT norm, a convex combination of L1 and L2 norms. We show by numerical simulation that the CLOT control is continuous and much sparser (i.e. has longer time duration on which the control takes 0) than the conventional EN (elastic net) control, which is a convex combination of L1 and squared L2 norms.

Keywords: Energy and Distribution Management Systems, Data-Driven Decision Making
Abstract: This paper develops a decision making tool to support the public decision maker in selecting the optimal energy retrofit interventions on an existing street lighting system. The problem statement is based on a quadratic integer programming formulation and deals with simultaneously reducing the energy consumption and ensuring an optimal allocation of the retrofit actions among the street lighting subsystems. The methodology is applied to a real street lighting system in Bari, Italy. The obtained results demonstrate that the approach effectively supports the city governance in making decisions for the optimal energy management of the street lighting.

Keywords: Social Networks
Abstract: Innovation or information propagation in social networks has been widely studied in recent years. Most of the previous works are focused on solving the problem of influence maximization, which aims to identify a small subset of early adopters in a social network to maximize the influence propagation under a given diffusion model. In this paper, motivated by practical scenarios, we propose two different influence minimization problems. We consider a Linear Threshold diffusion model and provide a general solution to the first problem solving a linear integer programming. For the second problem, we provide a technique to search for an optimal solution that works only in particular cases and discuss a simple heuristic to find a solution in the general case.

Keywords: Building Automation, Computational Social Sciences
Abstract: Efficient building energy management has attracted a great deal of academic interest with significant potential energy savings to be envisaged. Social scientists strive to achieve these savings by employing behavior-based approaches, while engineers investigate control strategies for the efficient operation of the building devices. This work can be seen as a first step towards bridging these two approaches by proposing a control scheme that encapsulates building occupant behavior into the energy management system. In particular, the occupants willingness to tolerate comfort bound violations is modeled as a random measurable uncertainty and incorporated into the building energy management system through disturbance feedback control policies. The respective optimal control problem is formulated as a mixed-integer stochastic optimization problem, and a computationally tractable approximation of it is derived by restricting the disturbance feedback control policies to admit an affine structure. An extensive numerical study verifies that the proposed approach can significantly reduce the energy consumption of the buildings.

Keywords: Energy and Distribution Management Systems
Abstract: According to the complementary characteristics of batteries and supercapacitors, different semi-active hybrid energy storage systems using just one dc/dc converter have been exploited in hybrid electric vehicles. To minimize the amplitude of the current in battery and energy loss in the system, this paper proposes an optimization scheme for the semi-active hybrid energy storage system under the traction mode and the regenerative braking mode. The energy distribution optimization is formulated as a convex problem and the primal-dual interior-point method is leveraged to acquire the optimal solution. The simulation results validate that the proposed scheme is efficient to minimize the amplitude of the current in battery with a reduced energy loss.

Keywords: Energy and Distribution Management Systems, Charging Stations for Plug-in Hybrid Electric Vehicles, Energy Storage Operation and Planning
Abstract: This paper presents a model-predictive framework for the optimal control of active cell-to-cell balancing in a lithium-ion battery pack. The framework addresses the tradeoff between minimizing (i) the state of charge (SOC) imbalance between battery cells and (ii) the energy dissipated by balancing. There is a rich existing literature on the design and control of pack balancing circuits. However, to the best of the authors’ knowledge, the use of model-predictive optimal control for multi-objective active balancing remains relatively unexplored. We solve the balancing problem using nonlinear model predictive control (NMPC), and exploit the differential flatness of battery cell dynamics to implement NMPC efficiently using pseudo-spectral optimization. A simulation study shows the effectiveness of the proposed approach for a two-cell balancing problem, but the approach can be generalized to longer battery strings.

Keywords: Charging Stations for Plug-in Hybrid Electric Vehicles, Energy and Distribution Management Systems, Energy Storage Operation and Planning
Abstract: This paper proposes a computationally efficient total least squares (TLS) framework for battery state of charge (SOC) estimation with a moving horizon approach. In the literature, battery SOC estimation problem assumes that the uncertainties come from (i) unmodeled dynamics and (ii) the output measurement noise. In contrast, the total least squares (TLS) estimation problem explicitly examines the noise in not only output measurements but also the input measurements. However, since both input and output variables are required to estimate, the computational burdens associated with the TLS estimation problem make online SOC estimation challenging. This paper exploits the differential flatness property of battery dynamics to increase the computational speed of the online TLS SOC estimation scheme. Since battery dynamics are differentially flat, one can represent battery dynamics using only one variable, the flat output. The exploitation of differential flatness makes moving horizon estimation (MHE) strategy very efficient due to two reasons: (i) it significantly decreases the number of decision variables and (ii) it eliminates battery dynamics equality constraints. The proposed framework is implemented to estimate battery SOC using an equivalent circuit model with the temperature dynamics (ECM-T) with a drive cycle input. By comparing the simulation results of a benchmark unscented Kalman filter (UKF), it shows that the proposed flatness-based MHE framework can provide more accurate SOC estimates.

Keywords: Linear systems, Robust control (linear case)
Abstract: In this study, a new method is proposed in order to analytically determine the stability boundaries of discrete time linear systems. The stability conditions of discrete time systems are reformulated in this method to derive an equation that includes the products of all eigenvalues, that can be checked more easily than conditions on every single eigenvalue. Thus, the time for calculating the space of all stable parameters of a given system can be greatly reduced. By further analysis, redundant products in the proposed method are eliminated in order to reduce the computational complexity more. The new procedure avoids bilinear transformations, decoupling at singular frequencies and discretization of the parameter space and doesn't include any conservatism. Further, interpretation of the proposed method in context of the Lyapunov stability is also given. Different case studies are included in the paper to prove correctness of the results and to illustrate the advantages of the proposed approach.

Keywords: Robust control (linear case), Robust control applications
Abstract: Permanent magnet synchronous motors (PMSMs) are widely used in industry due to their high torque per volume ratio, low noise, efficiency and wide stable operation region. Especially in home appliance applications, obtaining high torque using small motors and low audible noise has crucial importance. Therefore, conventional DC motors have been replaced by the permanent magnet synchronous motors in the modern applications. In this study, field oriented sensorless control over wide speed range is carried out under parametric uncertainties. The pole coloring concept is used for the control of a PMSM in an industrial setup for the first time. The design method and experimental results are presented.

Keywords: Robust control (linear case), Sliding mode control, Regulation (linear case)
Abstract: The output tracking of arbitrary references problem for discrete-time linear perturbed systems in the case of error-feedback and unknown external disturbances is solved approximately. A state observer is designed for the steady state error system based on the plant model and considering the tracking error as a measurable system output. Discrete super-twisting algorithm is used to design the robust sliding mode control. The main application of these techniques are the non-minimum phase systems, therefore, simulations show the effectiveness of the proposed techniques in this kind of systems.

Keywords: Time-varying systems, Robust control (linear case), Robustness analysis
Abstract: It is shown how to compute the aperture, or gap, between the finite-horizon graphs of linear time-varying dynamical systems, given state-space models of the continuous-time input-output maps. The approach involves four quadratic matrix first-order differential equations, three with the symplectic structure of Riccati equations. All four are subject to single-ended boundary conditions. So standard numerical methods can be used to compute the solutions as required. Three of the differential equations, two Riccati and the other not, need to be solved once to construct normalized graph representations, and to test an invertibility condition, respectively. When this invertibility condition is verified, the directed gaps are equal, and the remaining Riccati differential equation is solved repeatedly in a bisection search to determine one of these. Computation of both directed gaps to find the maximum would, by contrast, involve repeatedly solving two such Riccati differential equations, each in a bisection search.

Keywords: Linear multivariable systems, Robust control (linear case), Linear systems
Abstract: Vehicle suspension systems require good road handling and passenger comfort given typical terrain irregularities. Active suspension control meets these requirements by isolating car body motion from vibrations at the wheels. Active suspension has been a favorite subject in the automotive industry in recent years. One promising approach for active suspension system is based on a passive spring and an actuator connected in parallel. This approach aims to increase driving comfort by actively decreasing the motion of the vehicle's body at low disturbance frequencies and to improve energy efficiency by applying passive suspension components in higher frequencies. This paper examines optimal control solutions for achieving these goals and aims to improve the suspension performance over the existing systems. A new, recently developed actuator is modeled, linearized, and combined with a quarter-car model after which H-infinity control structures for both cascaded and direct control methods are designed and tested. Controller performance is evaluated by numerous simulations and input data from a real test track. The results are compared to a state-of-the-art controller implemented in an earlier project. The study shows the advantages of the new controllers over the existing one. The suspension performance is improved especially at low frequencies with no increase in energy consumption. Additionally, further increase in control bandwidth can be achieved if road-level measurements are utilized.

Keywords: Disturbance rejection (linear case), Robust control (linear case), Industrial applications of optimal control
Abstract: This paper proposes Multi Input and Multi Output (MIMO) robust Disturbance Feedback Control (DFC) design using Linear Matrix Inequalities (LMIs). DFC can be added to existing controllers as an additional control loop, to attenuate disturbances and model uncertainties. The extended state space representation of the overall system is considered with parametric model uncertainties. LMIs are formulated to solve the optimization problem such that the DFC satisfies Lyapunov stability and robust performance. DFC was applied in the superheat control and the suction pressure control for a refrigeration system, and experimental results shows that DFC improves disturbance rejection compared to conventional PI controllers.

Keywords: Fractional systems, Robustness analysis, Robust control applications
Abstract: Path planning is an essential stage for mobile robot control. It is more newsworthy than ever in the automotive context and especially for autonomous vehicle. Also, path planning methods need to be adaptive regarding life situations, traffic and obstacle crossing. In this paper, potential field methods are proposed to cope with these constraints and autonomous vehicles are considered equipped with all necessary sensors for obstacle detection. In this way, Ge&Cui’s attractive potential field and fractional attractive potential field have been adapted to the context of autonomous vehicles. In this way, this latter method ensures better stability degree robustness with controlled vehicle acceleration.

Keywords: Fractional systems, Systems with time-delays, Tracking
Abstract: This paper proposes a Fractional-order modification of the traditional Integral Resonant Controller named as FIRC. The fractional integral action utilised in the proposed FIRC is a simple, robust, and well-performing technique for vibration control in smart structures with collocated sensor-actuator pairs such as nanopositioning stages. The proposed control scheme is robust in the sense of being insensitive to spillover dynamics and maintaining closed-loop stability even in the presence of model inaccuracies or time-delays in the system. The experimental and simulated results have showed that the proposed FIRC can provide a closed-loop bandwidth which spans up to a 95.2% of the first resonant mode of the experimental system, thus improving the bandwidth achieved by classical integer-order IRC implementations.

Keywords: Fractional systems, Linear multivariable systems, Robust control (linear case)
Abstract: A fractional-order robust controller to control a reverse osmosis desalination plant is proposed. The dynamics of this plant was experimentally identified. A multivariable mathematical model was obtained which showed large plant parameter variations. The proposed control system is composed of a decoupler, a diagonal compensator of the less variable dynamics, and a fractional order PI controller in each of the two loops. The fractional orders of these last controllers were optimized in order to improve the phase margin robustness. It was shown that these controllers outperformed the robustness achieved using PI controllers.

Keywords: Fractional systems, Infinite-dimensional systems, Linear systems
Abstract: Automotive natural gas engines are designed to dramatically reduce the emission of polluting gases, particulate matter, and harmful substances associated to combustion of Diesel or gasoline fuels. Their performance depends on the accurate metering of the air/fuel ratio, which is pursued by controlling the gas injection timing and the pressure in the common rail volume. To achieve this goal, an accurate control-oriented model is necessary to represent gas pressure dynamics in the main parts of the injection system. This work focuses on the gas transition from inner parts of the injection system to the common rail. The aim is to identify a linear non-integer-order model that describes the process in a more effective and compact way than ARX integer-order models of high order. Identification is performed in a non-parametric setting, trying to identify the optimal smooth fit in the frequency domain by minimization of an appropriately selected discrepancy criterion, between the actual measurements and the model outputs. In this way, the model structure will be obtained as part of the identification process and will not be fixed a priori.

Keywords: Fractional systems, stability of distributed parameter systems
Abstract: In this paper sufficient stability conditions are established for fractional systems with perturbed differentiation orders. It is an extension of the recently published paper (Rapaic and Malti, 2016) which allows henceforth increasing the highest differentiation order. The maximum allowable variation on all differentiation orders is computed so that the stability (respectively instability) is preserved when differentiation orders are perturbed away from the commensurate ones. The maximum allowable variations are compared in both cases: when the highest order is allowed to be increased and when it is not. The established conditions allow concluding on the stability of incommensurate fractional systems on the basis of Matignon's theorem and the additional sufficient condition.

Keywords: Fractional systems, Robust controller synthesis, Parametric optimization
Abstract: This paper deals with a new structure of Fractional Complex Order Controller (FCOC) with the form PID^(x+iy), in which x and y are the real and imaginary parts of the derivative complex order, respectively. A tuning method for the Controller based on numerical optimization is presented to ensure the controlled system robustness toward gain variations and noise. This can be obtained by fulfilling five design requirements. The proposed design method is applied for the control of a Second Order Plus Time Delay resonant system. The effectiveness of the FCOC design method is checked through frequency and time domain analysis.

Keywords: Identification and control methods, Motion Control Systems, Human operator support
Abstract: We present a method to identify the physical parameters of rotational systems with compliant load coupling in a robust manner. To ensure sufficient excitation, a pseudo random binary signal (PRBS) is automatically parameterized based on the noise level in the available feedback signal. To identify the plant parameters, we rely on a two step procedure: Firstly, the user is asked to mark distinct features in the non-parametric estimate of the frequency response. These features serve to calculate initial parameter guesses. Secondly, a non-convex curve fitting problem is solved to refine the parameter estimates. Experimental results verify that the method allows to robustly identify parameters even for small amounts of raw data resulting from short measurement times.

Keywords: Motion Control Systems, Identification and control methods, Vibration control
Abstract: High throughput requirements on high-precision manufacturing systems lead to a situation where the flexible dynamics hamper the performance at the positions of interest. Since these points are typically not measured directly, high performance local control of measured positions may lead to deteriorated performance due to internal deformations. A possible solution is to employ a control strategy which ensures that the desired rigid body motion is achieved, without exciting the parasitic flexible dynamics. In this paper, a feedforward controller design procedure is developed that achieves this type of global performance, which in turn leads to increased performance at the unmeasured positions of interest. The proposed method is applied to an experimental wafer stage showing that the proposed approach indeed leads to superior results with respect to the traditional local control approach.

Keywords: Modeling, Design methodologies, Robots manipulators
Abstract: This paper deals with cable-based motor-to-joint transmissions of multi-link robots chain. Their effects on robots flexibility have to be taken into account for modeling and control design. More in details, slack cables do not provide any force during compression (unlike springs), may present an initial nonzero elongation (preload) and, depending on the material, could exhibit non-constant stiffness. Those features may lead to non-trivial piece-wise elastic torques in a mechanical transmission. In this context, we present a framework to generate a more general (piece-wise) elastic torque model which can be embedded in the classical flexible-joint robot model, coherently with the Lagrangian approach. Moreover, we propose a model based on polynomial stiffness, whose parameters can be identified with ordinary least-squares techniques. Experimental results conducted on a flexible transmission show the usage and the utility of this work.

Keywords: Robots manipulators, Robotics technology
Abstract: In this work, redundancy resolution has been employed to increase the Cartesian mechanical rigidity of 7 DOF robot manipulators during tasks requiring stiff interactions with the environment (e.g. milling or drilling). The Cartesian static stiffness of the end-effector for a given joint configuration is deduced from an identified joints stiffness model. The Cartesian reflected rigidity evolution over an analytically computed self-motion of the manipulator shows significant variations that clearly highlight the need to select the right set of joint angles among the possible ones. A global optimization scheme of the redundant DOF is proposed to determine the stiffest robot configurations for a given pose of the end-effector. An experimental study on 7 DOF KUKA LBR iiwa then shows the relevance of the proposed approach in finding the redundant robot joint angles that optimize this rigidity criteria.

Keywords: Vibration control, Smart Structures, Robots manipulators
Abstract: This paper proposes and studies the use of industrial robot manipulators to act as an active steady rest in machining thin-walled workpieces to prevent machining chatter instability. Feedback control is applied to the robotic steady rest in contrast to the other more popular active control method where the cutting tool is controlled. The entire dynamic system model is characterized by delay differential equations with time varying interactions between the tool side and work side dynamics. Semi-discretization, finite element modeling, and model reduction are applied to obtain a lower order plant model for the feedback control design. Control design and simulation results on turning of thin-walled tubes are presented to demonstrate the effectiveness of the proposed modeling and control approach for the robotic active steady rest.

Keywords: Smart Structures, Design methodologies
Abstract: This paper presents the mechanical structure optimization of a piezoelectric energy harvesters devoted to supply embedded animal tracking devices. The harvester mechanical structure being a bilayer unimorph piezoelectric cantilever is composed of two elements: a piezoelectric layer and a non-piezoelectric layer (i.e, active and passive layers). The suggestion of this paper is to find the optimal ratio between their two thicknesses in order to maximize the output recuperated voltage at the electrodes. For that, from the model that links the mechanical excitation moment and the output electrical charge, a gradient-based optimization is carried-out. Comparison with existing mechanical piezoelectric harvester structure is made and which clearly demonstrates that the proposed structure permits to gain up to five-times in terms of the output charge and a significant gain in terms of output electrical power for the same condition.

Keywords: Identification and model reduction, Modelling and decision making in complex systems, Methodologies and tools for analysis of complexity
Abstract: A minmax problem statement of a problem of the parameter identification of the input/output mapping of systems is considered, based on the quadratic Rényi entropy (in the sense) of the identification error. As well, the quadratic Rényi entropy is used to construct a measure enabling one to select input variables (structure identification as a preliminary step) of the model subject to parametric identification in an adequate manner that is justified with the properties of the measure proposed. A case study is considered, with confirming the full practical suitability of the theoretical considerations of the paper.

Keywords: Process supervision, Production activity control, Production planning and control
Abstract: The problem of the change point detection in a sequence of random variables is considered. The task arises in control of technological processes, particularly, in oil and gas production management. Some equipment parameters are to be controlled in order to detect a change of the equipment characteristics and, consequently, a breakdown of its technological regime. As a rule, the data observed are stochastic with the unknown distribution. In the paper a non-parametric method for detection a change in the mean or in the variance of data is developed and some modifications are proposed. All the algorithms do not use the information concerning the distribution function of observations before and after the change point. The algorithms are applied to detect change points of the characteristics of a gas well.

Keywords: Modeling of manufacturing operations, Manufacturing plant control
Abstract: The paper describes a new technique for estimation up to three points on a process frequency response from a single biased-relay feedback test without using the FFT algorithm. The new technique called “shifting method” extends possibilities of the relay feedback identification of dynamic systems describable by linear models. The introduced identification method is applicable for systems with dead time if there are stable limit cycles in biased-relay feedback tests. This approach enables to fit up to five parameters of a process transfer function from data observed by one relay experiment. The method may be used for parameter estimation of linear mathematical models with various structures and therefore it can be used for parameter fitting both isochronic and anisochronic linear models. This is demonstrated on proportional and integrating time delay processes described by isochronic or anisochronic linear models. The paper also introduces explicit formulas for the parameter estimation of the process model of the second order plus time delay. The applicability of the proposed method is demonstrated on simulated examples. Matlab/Simulink programming environment was used for all simulations.

Keywords: Flexible and reconfigurable manufacturing systems, Modeling of assembly units, Identification and model reduction
Abstract: This paper presents an attempt to improve dynamic identification procedure for robotic manipulators, such that obtained models are appropriate for trajectory planning and motion control. In addition to algorithms development, authors bring new insights and arguments for the experiments organisation and analysis of the collected data. Significant part of the work is devoted to a case study in calibrating of a collaborative robot (co-bot) KUKA LWR4+, which represents a powerful tool for modern manufacturing systems. Therefore, refined models compared to the ones relied on CAD data provided by the manufacturer can be beneficial for various applications.

Keywords: Konwledge discover (data mining), Model-driven systems engineering, Identification and model reduction
Abstract: The paper analyzes the capabilities of identification algorithms based on the intelligent data analysis to predict the approach of process parameters to critical limits, as well as their possible transition to chaotic dynamics. The results obtained are illustrated with an example from the oil refining industry.

Keywords: Multiagent systems, Identification and model reduction, Complex logistic systems
Abstract: The paper presents control methodology and algorithms for multi-agent systems based on predictive identification and simulation models. The efficiency of the technique proposed is understood as the stability of a compound dynamic system. An approach to system’s stability margin prediction based on associative search and wavelet analysis is offered.

Keywords: Intelligent transportation systems, Automatic control, optimization, real-time operations in transportation, Guidance, navigation and control of vehicles
Abstract: This study proposes an optimal and practical scheduling and sequencing algorithm to increase the capacity in handling inbound traffics through Point Merge System (PMS) and to support the decision of air traffic controllers. To design the optimization algorithm, performance index and several constraints which are required for PMS are derived. In addition, to extend the optimal algorithm in the practical point of view, the first-come-first-served order and constrained position shifting constraint are considered. Through numerical simulations, the performance of the optimal and practical algorithms are demonstrated.

Keywords: Modeling and simulation of transportation systems, Information processing and decision support, Man-machine interface in transportation
Abstract: An analysis framework is devised in which a trajectory prediction engine provides flight position and time estimates for the conflict detection engine. In this framework, many other important features are considered, namely flight intent modeling, predicted trajectory simplification and processing for the conflict detection, and multiple flight scenario simulation.

Keywords: Transportation logistics, Freight transportation, Modeling and simulation of transportation systems
Abstract: The Pickup and Delivery Problem, known as PDP, is one of the most combinatorial optimization problems studied in the literature. In this type of problems, loads must be transported by a fleet of vehicles from pickup sites to delivery sites. A set of constraints must be respected in relation with the capacity of the vehicles, the opening and closing times of each site. This paper presents the first metaheuristic method to solve a new variant of the PDP which we called SPDPTWPD (Selective PDP with Time Windows and Paired Demands). In this variant, the precedence constraints (paired demands) and the choice of sites to be served (selective aspect) must be considered. We proposed a hybrid genetic algorithm to deal with the multi-objective SPDPTWPD. We tested our proposed approach on benchmark instances and the obtained results show its efficiency.

Keywords: Transportation logistics, Modeling and simulation of transportation systems
Abstract: Home health care organizations are inclined to optimize their activities since the rise of the demand for home care. Defining the routing of the caregivers are complex activities to perform because of the different objectives to optimize and constraints to handle. The objectives considered are often in conflict leading one objective to deteriorate the other ones. Indeed, balancing the traveling time may increase the total traveling time of caregivers as well as increase the soft patient time window and shared visits non-satisfaction. Therefore, a good balance must be found between these objectives. Thus, we propose a general mixed-integer programming model for the home health care routing and scheduling problem with route balancing. The proposed model handles most of the known characteristics in order to be application-based independent. A memetic algorithm is proposed to evaluate the multi-objective approach on several instances and support decision-making. The impact of the focus on route balancing is analyzed on the behavior of the objectives. The results highlight that the focus on route length balancing may become harmful by deteriorating the other objectives while not improving anymore the maximal route length difference. Moreover, the route length balancing shows some different sensitivities on the other objective functions depending on the instance structure.

Keywords: Transportation logistics, Freight transportation, Modeling and simulation of transportation systems
Abstract: This paper proposes a particle swarm optimization algorithm to solve the capacitated location-routing problem(CLRP). A new encoding scheme is proposed to represent the solution of the CLRP. During the improvement procedure, several local search methods allow to improve the quality of each solution. Experiment tests are carried on some benchmark instances. Results show that our method is effective and simple.

Keywords: Information processing and decision support, Intelligent transportation systems, Multi-vehicle systems
Abstract: ulti-Aircraft Conflict Resolution (MACR) is a Multi-Criteria Decision-Making (MCDM) problem, which involves multiple stakeholders (airline, air traffic controller, and aircraft) with competing and incommensurable objectives. This paper proposes a two-step MCDM scheme to the solution of MACR. In the first step, a second order cone program is adopted to generate a set of candidate resolution strategies with different minimum separations between trajectories. Each candidate strategy is then evaluated via three criteria modeling the interests of the stakeholders. In the second step, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach is used to determine the best strategy that realizes an adequate tradeoff among the competing interests while coping with their incommensurability. Some numerical results are presented to show the efficacy of the proposed scheme. Interestingly, the minimum separations associated with the best resolution strategies according to either the interest of the airline or that of the aircraft both differ from the one adopted in the current air traffic control operation.

Keywords: Optimal operation and control of power systems, Control of renewable energy resources, Model predictive and optimization-based control
Abstract: In this work we formulate a control strategy for the control of a Wave Energy Converter (WEC) aiming to maximize power take off taking into account losses in the conversion from mechanical to electrical power. The analysis is based on a point absorber. Maximizing electrical power however results in large variations in the forces or torques in the structure implying large fatigue burden giving reduced life time or requirements for increased dimensions of the structure; therefore there is a tradeoff between harvested energy and demands to the construction. We suggest analysis of this involving a Model Predictive Control (MPC) strategy. Fatigue is usually assessed using the method of rainflow counting and Miner’s rule. This model is difficult to include in an MPC formulation, instead we chose to give torque in the shaft of the power take off a quadratic weight in the performance function and evaluate the fatigue from simulated results. The optimization of power take off relies on a model of losses in the power conversion. For the control we apply an approximated friction model. Simulations are performed using time-series of wave forms representing sea states typical for the intended location of the WEC. A Pareto front illustrates obtained mean power versus necessary dimensions due to fatigue. The results are compared with standard resistive and reactive controllers. The results show that the MPC produces more than 25 % more harvested energy than the reactive control for the same requirements for the dimensions.

Keywords: Control of renewable energy resources, Optimal operation and control of power systems, Modeling and simulation of power systems
Abstract: CCell is a type of Oscillating Surge Wave Energy Converter (OSWEC) under development by Zyba Renewables Ltd. Laboratory scale wave tank tests have been conducted with a prototype device equipped with a passive controllable hydraulic power take-off system. The results are used to derive a simplified semi-analytical model of the governing hydrodynamics, providing a basis for simulation studies of control algorithms. The Simple and Effective control strategy of Fusco and Ringwood (2013) is used to generate an angular velocity trajectory designed to maximise energy capture whilst keeping the device motion within physical displacement constraints. Velocity tracking is achieved using a State Feedback Control architecture with integral action (SFCIA) , combined with an integral position controller acting on measured angular position in order to reduce drift effects. Good performance when subjected to scaled real irregular sea states is observed in terms of energy capture and motion, and good robustness to parameter variation is demonstrated.

Keywords: Control of renewable energy resources, Filtering and estimation for FDI, Control system design
Abstract: The implementation of the majority of energy maximising control strategies requires the knowledge of the wave excitation force experienced by the wave energy converter (WEC). In addition, many optimal numerical control strategies also require future knowledge, or a forecast, of future values of the excitation force. This paper examines both the excitation force estimation and forecasting problem for a heaving buoy wave energy device. In particular, a Kalman filter is used to estimate excitation force, where the wave force model is comprised of a set of oscillators at discrete frequencies. The forecasting algorithm consists of an autoregressive model, where the value of prefiltering, in terms of forecasting performance, is evaluated. The paper provides a level of sensitivity analysis of the estimation and forecasting performance to variations in sampling period, sea spectral shape factor and prediction horizon. Results demonstrate that the achievable performance of the estimator/forecaster is consistent with the broad requirements of numerical optimal WEC control strategies (Fusco and Ringwood (2012)), which depends on the characteristics of the radiation impulse response.

Keywords: Control of renewable energy resources, Nonlinear process control, Control system design
Abstract: In this paper, the non-linear effects of viscosity on the performance of a Wave Energy Converter (WEC) system are analysed. A standard linear Model Predictive Control (MPC) is used to show the negative effects that the unaccounted non-linear viscosity force in the hydrodynamic system has on the power absorption. A non-linear MPC (NLMPC) is then implemented, where the non-linear viscosity effects are included in the optimisation. A linear drag coefficient estimate of the non-linear viscosity is then included in the linear MPC; creating a Linear Viscous Model Predictive Control. When constraints are incorporated, it is shown that a single choice of the linear viscous drag coefficient for use within the linear MPC can provide comparable results to the non-linear MPC approach, over a wide range of sea states.

Keywords: Control of renewable energy resources
Abstract: Passive loading is a suboptimal method of control for wave energy converters (WECs) that usually consists of tuning the power take-off (PTO) damping of the WEC to either the energy or the peak frequency of the local wave spectrum. Such approach results in a good solution for waves characterized by one-peak narrowband spectra. Nonetheless, real ocean waves are non-stationary by nature, and sea wave profiles with different spectral distribution occur in a specific location over time. Thus, the average energy absorption of passively controlled WECs tends to be low. In this paper, we propose a real-time passive control (PC) based on the Hilbert-Huang transform (HHT), where the PTO damping is time-varying and tuned to the instantaneous frequency of the wave excitation force. The instantaneous frequency is calculated by using the HHT, an analysis method for nonlinear and non-stationary signals that relies on the local characteristic time-scale of the signal. A performance comparison (in terms of energy absorption) of the proposed solution with the passive loading method is presented for a heaving system, in a variety of wave spectra. It is shown that a performance improvement of up to 21%, or 65%, is obtained for the proposed PC scheme, when it is compared to passive loading tuned to the energy, or the peak frequency of the spectrum, respectively. Real ocean waves off the west coast of Ireland are adopted in the simulations.

Keywords: Control system design, Advanced control technology, Control of renewable energy resources
Abstract: This paper considers hierarchical control strategy (HCS) for maximising power conversion between mechanical and electrical powers in heaving wave energy converters. The maximisation conversion were obtained by designing the optimum reference for the buoy. This reference were found by relating the mechanical and electrical models of the power-takeoff (PTO) device. A simple look-up table of the intrinsic resistance constant was obtained as function of wave’s significant height and peak frequency. A Robust PID controller was used to track this reference. The PID controller was tuned using complex polynomial stabilisation method to convert the robust performance specification into a set of linear programming problem. The interesting feature of the method is all (not only one) set of PID parameters, which satisfies the robust performance, were found. Finally, the simulation results were presented to verify the control objectives.

Keywords: Biomedical system modeling, simulation and visualization, Kinetic modeling and control of biological systems
Abstract: Whereas obtaining a global model of the human endocrine system remains a challenging problem, visible progress has been demonstrated in modeling its subsystems (axes) that regulate the production of specific hormones. The axes are typically described by Goodwin-like cyclic feedback systems. Unlike the classical Goodwin oscillator, obeying a system of ordinary differential equations, the feedback mechanisms of brain-controlled hormonal regulatory circuits appear to be pulsatile, which, in particular, exclude the possibility of equilibrium solutions. The recent studies have also revealed that the regulatory mechanisms of many vital hormones (including e.g. testosterone and cortisol regulation) are more complicated than Goodwin-type oscillators and involve multiple negative feedback loops. Although a few “multi-loop” extensions of the classical Goodwin model have been studied in the literature, the analysis of impulsive endocrine regulation models with additional negative feedbacks has remained elusive. In this paper, we address one of such models, obtained from the impulsive Goodwin-type oscillator by adding an additional linear feedback. Since the levels of hormones’ concentrations oscillate periodically, examination of endocrine regulation circuits is primarily focused on periodic solutions. We prove the existence and uniqueness of periodic solutions of a special type, referred to as 1-cycles and featured by the unique discontinuous point in each period. Procedures for computing such a solution and testing its stability are discussed. The results are confirmed by numerical simulations.

Keywords: Control of physiological and clinical variables, Artificial pancreas or organs, Chronic care and/or diabetes
Abstract: A standard mathematical model of the glycemia/insulinemia dynamics is in continuous time and goes with discrete time measurements of the subcutaneous glycemia delivered by a continuously glucose monitor (CGM). An textit{ad hoc} observer for linear models with discrete output measurements is experimented for such biomedical systems. It is based on the impulsively observability over a finite-time horizon and its corresponding Kalman - like criterion to check asymptotic convergence. The method depends on the allocation of eigenvalues of an impulsive system and the Hautus criterion for an underlying discrete-time system. Finally, the performance of the impulsive observer is tested through 50 hours estimation on 3 patients using their clinical data. These preliminary results motivate an extensive study of this class of observers for biomedical processes, in particular, for the case of T1DM patients. It represents an economical way to estimate the states such as blood glucose, insulin and carbohydrates concentration. Their estimates may eventually be used in a closed-loop for the design of an artificial pancreas.

Keywords: Artificial pancreas or organs, Decision support and control, Control of physiological and clinical variables
Abstract: In this work the problem of regulating glycemia in type I diabetic patients is studied by means of an impulsive zone model predictive control (impulsive ZMPC) based on a novel long-term glucoseinsulin model. Taking advantage of the model - which features real life properties of diabetes patients that some other popular models do not - the proposed control ensures the stability under moderate-tosevere disturbances. A long-term scenario - including meals - are simulated, and the results appear to be satisfactory as long as every hyperglycemia and hypoglycemia episodes are suitably controlled.

Keywords: Biomedical system modeling, simulation and visualization, Physiological Model, Chronic care and/or diabetes
Abstract: In this contribution, a novel method for model order reduction is presented. Since biological systems are often highly nonlinear or having a high number of parameters, the concept of empirical Gramians is considered for controllability and observability analysis. This allows a balanced representation of the system, which offers model reduction by truncation. We motivate to transfer the idea of balancing states to of balancing parameters. For that, loadability Gramian is introduced first, characterizing controllability Gramian for parameters. Second, parameters were treated as constant states, which allows observability-based identification. It is then possible to obtain a balanced realization of the parameter-space of the system, which enables balanced truncation. This method is applied to systems of glucose-insulin homeostasis.

Keywords: Cellular, metabolic, cardiovascular, pulmonary, neuro-systems, Dynamics and control, Model formulation, experiment design
Abstract: This paper studies possible bifurcations and synchronization of subpopulations of a class of macroscopic models called neural mass models. These models describe the mean activity of entire neural populations, represented by their averaged firing rates and membrane potentials. Connections between the nodes represent the static nonlinear sigmoidal function. We study local bifurcations and make a global stability analysis for one subpopulation of the neural mass model. Also we consider the behavior of two coupled subpopulations and find the sufficient conditions of their synchronization.

Keywords: Cellular, metabolic, cardiovascular, pulmonary, neuro-systems, Modeling and identification
Abstract: Canalyzing Boolean functions have shown their popularity in various biological networks and established themselves to be biologically meaningful at the system level, marking their importance in the analysis of stability and robustness of such complex systems. Based on a matrix representation of Boolean networks due to the recently developed tool called semi-tensor product, we categorize canalyzing functions in terms of their capabilities of affecting the number of attractors in the Boolean network, which is one key index for the stability and robustness of Boolean networks. We show that there exist only three categories of attractor-effective canalyzing functions for any network size larger than 1, while the number of all the interested canalyzing functions is proportional to the square of the network size. We also give the explicit expression of the mean number of attractors with any length for Boolean networks with a single canalyzing function. Compared with Boolean networks without any canalyzing functions, we are able to show quantitatively how canalyzing functions can affect the mean number and length of attractors in Boolean networks for the first time.

Keywords: Process performance monitoring/statistical process control, Statistical methods/signal analysis for FDI
Abstract: In most chemical processes, both online measurements and offline laboratory analysis can be obtained at different sampling rates. Usually, the online process variables are frequently sampled while the key quality indicators are analyzed at irregular time, sometimes on hourly and sometimes daily basis. To effectively integrate different classes of measurements in a multi-rate process, a multi-rate probability principal component analysis (MPPCA) model is proposed to utilize the efficiently collected data and to improve the performance on both model prediction and process monitoring. In MPPCA, the model parameters are calibrated by the expectation-maximization algorithm. The proposed method is able to handle irregular samples in measurements and incorporate all the observations for model training. Also, the corresponding statistics based on MPPCA is developed for the fault detection purpose. Finally, a TE benchmark is presented to illustrate the effectiveness of the proposed methods.

Keywords: Active Fault Diagnosis, Fault accommodation and Reconfiguration strategies
Abstract: For complex batch processes, it is possible to encounter the problem of singularity of kernel matrix during the calculation of kernel Fisher discriminatory analysis (KFDA) model. In this paper, an improved KFDA algorithm is proposed for fault diagnosis of nonlinear batch processes. Firstly, the original data is projected from the original space to high dimensional space by kernel functions. Secondly, in the calculation of KFDA, the orthogonal matrix is obtained by singular value decomposition for kernel within-class scatter degree matrix. Finally, the processed data and kernel within-class scatter degree matrix is projected onto a nonsingular orthogonal matrix after the decomposition. The fault diagnosis experiment results shows that the feasibility and efficiency of the method in beer fermentation process.

Keywords: Process performance monitoring/statistical process control, Data mining and multivariate statistics, Industrial applications of process control
Abstract: Multivariate Statistical Process Control (MSPC) techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) have found wide application especially in the statistical modeling and monitoring of chemical processes. However, real industrial processes often violate the assumptions underlying MSPC since they exhibit time-varying and non-stationary behavior. Adaptive PCA-based monitoring procedures such as Moving Window PCA (MWPCA) and Recursive PCA (RPCA) have been proposed to tackle this issue. Although the parameter selection for those procedures is critical to their proper implementation, this topic is rarely covered in the literature.

This paper examines two methods for MWPCA and RPCA parameter selection using the Tennessee Eastman process as an example. Based on the findings a novel procedure for RPCA parameter selection as well as a extension to RPCA will be proposed and demonstrated.

Keywords: Process performance monitoring/statistical process control, Process observation and parameter estimation, Process modeling and identification
Abstract: The forecasting of information in industrial process control can assist in predicting the occurrence of future events. This is useful when such events are undesired and may lead to costly shutdowns of an industrial process. Thus, the forecasting of information can assist in determining preparatory measures to be taken to mitigate or prevent the occurrence of undesired events. This paper addresses the forecasting of information in industrial process control using a hidden Markov model approach. It defines three types of information signals from data of industrial process control (namely, low-dynamic, fast-dynamic, and multi-level switching). The paper discusses the complete methodology for forecasting of the signals; namely, pre-processing of the data, detection of the explanatory variables, determining the order of the forecasting model, implementation of the forecasting model, and generation and validation of the forecasted information. In addition, the paper implements the proposed methodology for two different practical systems with adjusting the parameters of the prediction model to demonstrate its applicability.

Keywords: Observer based and parity space based methods for FDI, Statistical methods/signal analysis for FDI, Industrial applications of process control
Abstract: In this paper, two common statistics, the T^2 and the Q statistics, for fault detection and process monitoring are compared. Specifically, the geometric relationship between the T^2 statistic and 3 common forms of the Q statistics is analysed. Furthermore, using the false alarm rate (FAR) and the fault detection rate (FDR), the fault detection performance of both statistics is quantified and compared. The results show that, for a given significance level, the T^2 statistic has the best overall FDR.

Keywords: Fault accommodation and Reconfiguration strategies, Reconfigurable control, sensor and actuator faults, Design of fault tolerant/reliable systems
Abstract: The focus of this work is to give some insight into the effects of sensor faults on the stability and performance of sampled-data state feedback systems, and how these effects can be mitigated through use of fault-tolerant control. These methods are applied to a linearized model of a sampled-data system with uncertainty and access to full state measurements. The stability of the system is explicitly characterized as a function of the state sampling rates, fault parameters, fault accommodation measures, and state matrices. Sensor faults are discussed that manifest as improper sensor readings, and sensor sampling rate drift. The first fault updates the model of the system at sampling times to a factor of the actual measurement, the second type of fault alters the rate at which the sensors sample the system. In the presence of these faults the locations of the closed-loop poles are altered to achieve stability of the faulty system. Furthermore a chosen performance metric of the closed loop system is parameterized the same way as the stability of the system, for further insight into post fault operation. The explicit characterization of the stability and performance landscapes offer a look into the robustness and margins of tolerable faults that can be accommodated for and these benefits are discussed with some simulation results for context.

Keywords: Optimal operation and control of power systems, Smart grids, Control system design
Abstract: This paper proposes an optimal power flow to supervise several distribution networks connected to a transmission grid. MV grids can support the voltages of the HV network by controlling adequately the reactive power exchange at the HV/MV interface. The interaction between the distribution grids equipped with predictive Volt-Var Controllers and the optimal coordination is investigated. From a case study involving a rural network, several recommendations are given to tune and combine these controllers.

Keywords: Modeling and simulation of power systems, Control system design
Abstract: The paper deals with controlling an electric power system comprised of a Hydrogen Fuel Cell (HFC), boost and boost/buck DC-DC power converters and the Ultracapacitor(UC) for an auxiliary power supply in order to control servomotor speed within a vehicle application. Relative degree approach is applied for direct control of the servomotor input voltage and speed, as well as the HFC and UC current in the presence of the model uncertainties. The non-minimum phase property of the DC-DC boost converter is eliminated by controlling the HFC and UC current based on the power balance approach. The adaptive-gain second order sliding mode controllers (2-ASMC) control the current in HFC and the servomotor speed. The conventional Sliding Mode Controllers (SMC) are designed for controlling the output voltage of the converter and the load current of the UC. The efficiency and robustness of the proposed SMC and 2-ASMC are confirmed via computer simulations.

Keywords: Modeling and simulation of power systems, Power systems stability, Control system design
Abstract: Embedded systems are using more and more electric motors. To reduce the volume and weight it is important to share the static power conversion devices. Mono-Inverter Multiple-Permanent Magnet Synchronous Motor (MIMPMSM) system is an effective solution to this purpose. In this paper, a controller of MIMPMSM system containing N (N≥2) PMSM is proposed. It is designed through analyzing the existence of finite steady state operating point. Based on the conclusions of analysis, two control strategies: master-selection and non-master-selection are discussed. The simulation has demonstrated their effectiveness in different operating scenarios.

Keywords: Power systems stability, Intelligent control of power systems, Modeling and simulation of power systems
Abstract: The solution of Lyapunov equations can be represented as a sum of Hermitian matrices corresponding either to particular eigenvalues of the system matrix, or to pairwise combinations of these eigenvalues. These eigen-parts or sub-Gramians proved to be useful for the stability analysis of electric power systems. In this paper we compare and contrast the sub-Gramians and participation factors as applied to the power system state estimation. Using the sub-Gramian approach we introduce the energy participation factor as a new indicator for selective modal analysis. For a stable system it characterizes the participation of i-th mode and initial k-th state in the integrated energy produced in k-th state. We explain the conceptual meaning and practical usefulness of energy participation factors and contrast them with the conventional participation factors in a selective modal analysis of the IEEE 57-bus test model.

Keywords: Application of power electronics, Modeling and simulation of power systems, Control system design
Abstract: A first order sliding mode controller with appropriate parameters sliding surface (modeled in usual time domain and realized by transfer function) is considered here for LCL grid connected three phases three wires shunt active filter (SAF). If shunt active conditioners are already well known in compensation for the main types of current disturbances in the electrical power systems, it is also admitted that they generate some undesired components caused by VSI switching frequency. In order to prevent these components from spreading to the grid side, a LCL output filter is generally proposed. In this context, a VSI connected grid via a LCL filter is largely proposed for renewable energy systems, where the component to be injected into the grid is only the fundamental. Unfortunately, when the injected components include fundamental plus harmonics and for a LCL output filter associated to linear controllers, a phase shift appears, between the identified harmonics current and the injected current. This phase shift impacts negatively the current disturbances filtration and/or compensation of the SAF. Therefore, sliding mode controller with appropriate sliding surface, in both time and frequency domains, is proposed as nonlinear control method, to overcome the phase shift effects over the entire bandwidth, for the shunt active filter. Besides, a PWM-SM-Controller, with zero order hold in input of the PWM, will allow to operate in a fixed frequency, preventing a variable switching frequency effects. The rapidity, tracking and robustness of this proposed controller, within the SAF, are validated by Matlab, Simulink, Simscap-Sim_Power_System code.

Keywords: Modeling and simulation of power systems
Abstract: This paper explains, from a systemic viewpoint, the importance to take into account the dynamic structure of the whole system in order to simplify the distributed parameters model of the transmission lines. Usually, the latter is approximated without considering its connection with the rest of the system, by comparing only its input-output behaviour with the one of the simplied model. Here, it is shown that this way to do can lead to biased results. More precisely, it is shown that the short-lines hypothesis leads to a reduction link with the -model but does not indicate clearly which dynamics have to be kept in the simplied model as well as their number. This is illustrated by considering the voltage collapse phenomenon. From this analysis, a more systemic approximation way is proposed to reduce subsystems of a general complex system. In power systems eld, all these investigations can help to improve the models used for simulation and control synthesis. Especially, to better connect the models to the specic phenomena which have to be reproduced in simulation.

Keywords: Kalman filtering techniques in automotive control, Vehicle dynamic systems, Intelligent transportation systems
Abstract: Control applications in the domain of autonomous vehicles need robust, accurate and fast motion estimators. Estimation techniques generally fuse together absolute and relative positions. This paper presents another approach which computes the system relative motion thanks to linear accelerometers and without rate gyroscopes. This method, known as Gyroscope-Free (GF), allows relative motion to be computed using only data from multiple accelerometers spread all around the object. A major aspect of this contribution is to get rid of any dynamic system model in the estimator such that the method proposed can be applied to any real application. A GF-Inertial Measurement Unit (IMU) relative motion estimator using an Extended Kalman filter (EKF) without system model is introduced and implemented on a real world test bench. Results show the suitability of the proposed estimator.

Keywords: Vehicle dynamic systems, Modeling, supervision, control and diagnosis of automotive systems
Abstract: This paper concerns the estimation of the sideslip dynamics of automotive vehicles in a bounded-error framework. This work considers the linear varying parameter (LPV) model of an automotive vehicle in a polytopic form. This model is obtained by considering the dependency of the model with respect to the vehicle speed as a varying parameter. Since this parameter belongs to a bounded interval (the vehicle speed is obviously bounded by constructor constraints), interval analysis tools propose efficient approaches for estimating the vehicle unmeasured dynamics in all situations. Indeed, in this paper, an estimation strategy is developed to reconstruct the vehicle sideslip dynamical behaviour and then proceed to a stability analysis of the car based on the driving situation. Simulations performed on a nonlinear vehicle model and its parameters (validated by an experimental procedure on a real Renault Megane Coupé) are used to validate the estimation strategy and highlight its efficiency.

Keywords: Vehicle dynamic systems, Automotive system identification and modelling, Kalman filtering techniques in automotive control
Abstract: This paper presents a modular observer structure to estimate the tire-road forces robustly, avoiding the use of any particular tire model, and using standard signals available in current passenger vehicles. The observer consists of a feedforward longitudinal force estimation block and a hybrid lateral force estimation module formed by an Extended Kalman Filter and a Static Neural Network Structure. Road grade and bank angle are estimated using sensor fusion, where a Fuzzy Logic controller combines the outputs from a Euler Kinematic model and a Recursive Least Squares block. The proposed observer is tested and verified using the simulation software IPG CarMaker under realistic driving situations. Lastly, the feasibility of the longitudinal force block is proved with real-time experiments.

Keywords: Automotive system identification and modelling, Vehicle dynamic systems, Kalman filtering techniques in automotive control
Abstract: Recent control and stabilization algorithms in driving security systems are based on the knowledge of vertical vehicle dynamics. In this article, a new methodology to estimate wheel-ground contact normal forces is presented. The proposed method use measurements from available sensors, like accelerometers, to update the static vertical forces while the vehicle is performing a mission, and suspension deflection sensors to determine the initial weight distribution. The primary goal of this study is the estimation of wheel-ground contact normal forces accurately while diminishing its dependence on the deflection sensors. One characteristic of this approach is the initial weight computation on each tire based on a multi-model technique and the Kalman filter. The algorithm's response is validated with wheel force transducers measurements from a real experiment.

Keywords: Automotive system identification and modelling, Learning and adaptation in autonomous vehicles, Kalman filtering techniques in automotive control
Abstract: Particle Gibbs with Ancestor Sampling (PGAS) is a particle Markov chain Monte Carlo method (PMCMC) for Bayesian inference and learning. PGAS conditions on a referencestate trajectory in the underlying particle lter using ancestor sampling. In this paper, we leverage PGAS for identication of cornering-stiness parameters in road vehicles only using production-grade sensors. The cornering-stiness parameters are essential for describing the motion of the vehicle. We show how PGAS can be adapted to eciently learn the stiness parameters by conditioning on the noise-input trajectory instead of the state trajectory. We verify on a three-minute long experimental test drive that our method correctly identies the tire-stiness parameters.

Keywords: Vehicle dynamic systems, Sensor integration and perception, Positioning Systems
Abstract: This paper proposes an inertial measurement based sideslip angle estimation for automotive applications. Sideslip angle influences many aspects of vehicle dynamics and stability, and many vehicle dynamics control systems either directly or indirectly control it. Its direct measurement is however costly; in most cases, control systems are thus designed around a sideslip angle estimator. Over the years, researchers have proposed many approaches. Most solutions are based on accurate dynamical models of the car. In order to be robust, these solutions need to also implement a real time surface friction estimation. This increases their complexity and affects their robustness. The present study proposes an inertial based approach that does not require the knowledge nor the estimation of road friction. This considerably simplifies the design, tuning and validation of the approach. Focus is devoted to the study of the effect of measurement errors. An extensive experimental validation confirms that the estimate is accurate and robust to a wide range of driving scenarios

Keywords: Stochastic hybrid systems, Stability and stabilization of hybrid systems, Optimal control of hybrid systems
Abstract: This paper addresses the optimal regulator problem for dynamic systems which evolve on discrete time domains where the time steps form a sequence of independent, identically distributed random variables. We introduce generalizations of the dynamic and algebraic Riccati equations and use them to find optimal gain matrices in both the finite-horizon and infinite-horizon case. Examples are presented.

Keywords: Hybrid and switched systems modeling, Stability and stabilization of hybrid systems, Switching control
Abstract: This paper proposes an algorithm to find a common zeroing-output system (CZOS) that plays a key role in studying uniform global exponential stability for a class of switched linear time-invariant systems under arbitrary switching. When a quadratic common Lyapunov function is hard to find while a quadratic common weak Lyapunov function is available, the concepts of CZOS can be used to assist in checking uniform global attractivity. When a CZOS for a given switched linear time-invariant system exists, a recursive algorithm is proposed to systematically find this CZOS. One example demonstrates the effectiveness of the proposed algorithm.

Keywords: Stability and stabilization of hybrid systems, Hybrid and switched systems modeling
Abstract: This paper deals with the stability analysis of a class of switched linear systems evolving on an arbitrary time domain. This class consists of a set of unstable linear continuous-time and unstable linear discrete-time subsystems. Using the time scale theory, some sucient conditions are derived to guarantee the exponential stability of this class of systems with all modes unstable. Simulation results show the eectiveness of the proposed scheme.

Keywords: Stability and stabilization of hybrid systems
Abstract: In this paper a novel nonlinear integral inequality on time scale is proposed. This inequality is applied to analyze stability of zero solution of quasilinear dynamic equations on time scale. Also, stability conditions are established for a wide class of nonlinearities in the system of dynamic equations.

Keywords: Stability and stabilization of hybrid systems, Event-based control
Abstract: This paper deals with some generalizations of certain inequalities of Pachpatte-type, on time scales, using elementary analytic methods. To show the feasibility of the obtained inequalities, an illustrative example is also introduced.

Keywords: Reachability analysis, verification and abstraction of hybrid systems
Abstract: Positive reachability and positive observability of positive linear time-variant systems on non-uniform time domains are studied. Such systems are treated as particular cases of linear systems on arbitrary time scales. Differential and integral calculus on time scales allows for unified treatment of continuous- and discrete-time systems. Positive reachability and observability are characterized with the aid of associated reachability and observability matrices. It is shown that the properties are dual to each other. Two types of discretizations of continuous-time systems are considered: Euler discretization and sampling. It is shown that both discretizations preserve positive reachability.

Keywords: Modeling of human performance, Modeling of HMS, Shared control, cooperation and degree of automation
Abstract: In this paper, we consider the Mental Workload (MWL) classification problem based on the measured physiological data. Firstly we discussed the optimal classifier structure from two perspectives of Convolutional Neural Network (CNN) depth (i.e., the number of hidden layers) and parameter optimization algorithm. The base CNNs designed were tested according to Accuracy and the model training time required. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the robustness and accuracy of a single CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of a series of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve the MWL classification performance and is characterized by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

Keywords: Shared control, cooperation and degree of automation, Engineering methods for HMS, Identification and control methods
Abstract: The theory of dynamic games has received considerable attention in a wide range of fields. While great effort has been made to develop new algorithms for finding Nash equilibria in dynamic games, the identification of cost functions has received little attention. We present an identification algorithm for linear quadratic dynamic games, a framework which can be applied in the field of shared control between a human and an automatic controller. In this application, the cost function describing human behavior is identified, taking into account the influence of the automation. Furthermore, we consider that human movement underlies certain variability by using a probabilistic Inverse Reinforcement Learning approach. As identification is performed in a single optimization step, the proposed method is suited for real-time applications. A simulation example shows that the algorithm successfully identifies the cost function of the first player which—in combination with the second player—reproduces the observed system output.

Keywords: Shared control, cooperation and degree of automation, Engineering methods for HMS, Identification and control methods
Abstract: This paper presents research on inverse optimal control in non-cooperative differential games. An inverse optimization framework for the identification of an unknown objective function of a game's participant is introduced. The identified objective function yields the same trajectories as the ground truth objective function. We apply the presented methods in a human-machine cooperation scenario, where identification of human behavior is indispensable for automation design. The framework's methods are evaluated through simulation of a driver cooperating with a driving assistance system based on a realistic vehicle dynamics model. The identification results are highly accurate and calculation performances motivate real-time applications.

Keywords: Shared control, cooperation and degree of automation, Modeling of HMS, Human operator support
Abstract: Humans can rapidly change their low-frequency arm dynamics (i.e., stiffness) to resist forces or give way to them. Quantifying driver’s time-varying arm dynamics is important for the development of steer-by-wire systems and haptic driver support systems. Conventional LTI identification, and even time-varying techniques such as wavelets, fail to capture rapidly-varying low-frequency dynamics. In this study, we propose to estimate driver admittance in real-time, using grip force measurement of the hands on the steering wheel and linear parameter-varying (LPV) modeling techniques. We hypothesized that grip force is strongly correlated to neuromuscular admittance, and can serve as an appropriate scheduling variable for an LPV model. We performed an experiment in which 18 subjects performed a boundary tracking task, and applied torque perturbations to the steering wheel to perform a baseline LTI identification. Six different boundary widths were used to evoke changes in admittance, while their grip force was measured with pressure gloves. A global LPV model is identified by linear interpolation between the local LTI models identified for each boundary width. The estimated stiffness and damping parameters varied proportionally with the grip force. Although small between-subject variations in grip force levels are found, we conclude that grip force can indeed serve as an appropriate scheduling variable for a global LPV model, which is capable of tracking fast-changing admittance changes. Future work focuses on using the LPV model in realistic driving tasks, permitting admittance estimates to be obtained without the need to apply external disturbance torques on the steering wheel.

Keywords: Task allocation-sharing and job design, Human operator support, Work in real and virtual environments
Abstract: This study is concerned with the shared object manipulation problem in a physical Human-Robot Interaction (pHRI) setting. In such setups, the operator manipulates the object with the help of a robot. In this paper, the operator is assigned with the lead role, and the robot is passively following the forces/torques exerted by the operator. We propose a controller that is free from the well-known translation/rotation problem and enhances the operator's ability to move the object by reducing the human effort. The key point in our study is that the controller is defined based on the instantaneous center of rotation. The passivity of the system including the object and the manipulator has been evaluated. Simulation results validate the theoretical findings on different scenarios of subsequent rotations and translations of the object.

Keywords: Shared control, cooperation and degree of automation, Human operator support, Human centred automation
Abstract: The situational awareness, robustness and expertise unique to skilled operators have precluded the full automation of many construction tasks. However, automatic control may be able to execute some quasi-repetitive construction tasks more efficiently than a human. This dichotomy has led to interest in Shared Control (SC) which synthesizes the advantages of both autonomous and manual control of machinery. A particularly interesting case of SC for unstructured environments is called Blended Shared Control (BSC) where weighted inputs from the human operator and autonomous controller are continuously combined to perform a task. We discuss the kinematics and dynamics of an excavator, and some low-level control and coupling issues. Then, we present a 1/12th scale hydraulic excavator test-bed, which is utilized for shared control experimentation. We introduce a state-machine based prediction framework and a BSC implementation which can be executed in real-time on embedded hardware. The proposed framework uses subgoals, distinct points in the workspace which the operator must reach in order to complete the task. We then analyze the performance of this BSC implementation for a typical earth-moving task with several operators on the excavator test-bed. Digging efficiency of each operator increases under BSC, whereas the effect of BSC on cycle time varies with operator skill level and operation style.

Keywords: Intelligent manufacturing systems, Intelligent maintenance systems, Holonic manufacturing systems
Abstract: This paper deals with the risk of non-ethical behaviour of future cyber-physical industrial systems designed by researchers. The will of the authors is to foster researchers working on these innovative systems to pay attention to the possible consequences of their design on the welfare of humans interacting with these systems and their possible responsibility in case of an accident. Mainly focused on robotics, the literature relevant to ethics is shown to be scarce in the field of cyber-physical industrial system while relevant stakes are important. A set of recommendations is suggested and an example dealing with the industrial deployment of a cyber-physical system in transportation illustrates these recommendations.

Keywords: Intelligent manufacturing systems, Flexible and reconfigurable manufacturing systems, Device integration technologies
Abstract: Additive Manufacturing (AM) is presenting an increasing growth in the last decades regarding the production of mechanical parts, due to its outstanding characteristics in relation to conventional processes. Indeed, AM can produce parts with unique geometries at once, which could otherwise require a lot of different operations or processes. However, like subtractive and forming processes, AM is prone to geometrical variations, which can affect the fit and function and even aesthetic features of its produced parts. This work aims to describe a research on the main AM processes regarding sources of dimensional variations in the mechanical parts production. The resulting analysis can be useful to provide designers with ways of geometrical errors compensation and correction in AM related designs.

Keywords: Intelligent manufacturing systems, Internet-of-Things, Quality assurance and maintenance
Abstract: Initiated from services and consumer products industries, there is a growing interest in using Internet of Things (IoT) technologies in various industries. In particular, IoT-enabled cybermanufacturing starts to draw increasing attention. Because IoT devices such as IoT sensors are usually much cheaper and smaller than the traditional sensors, there is a potential for instrumenting manufacturing systems with massive number of sensors. The premise is that the big data subsequently collected from IoT sensors can be utilized to advance manufacturing. Therefore, data-driven statistical process monitoring (SPM) is expected to contribute significantly to the advancement of cybermanufacturing. In this work, the state-of-the-art in cybermanufacturing is reviewed; an IoT-enabled manufacturing technology testbed (MTT) was built to explore the potential of IoT sensors for manufacturing, as well as to understand the characteristics of data produced by the IoT sensors; finally, the potentials and challenges associated with big data analytics presented by cybermanufacturing systems is discussed; and we propose statistics pattern analysis (SPA) as a promising SPM tool for cybermanufacturing.

Keywords: Intelligent manufacturing systems
Abstract: Nowadays, the introduction of digital technologies in the manufacturing industries paved the way to the Fourth Industrial Revolution, also known as Industry 4.0, fostering the evolution of traditional industrial systems to the concept of smart manufacturing. In last years, many reference models to describe the features of the smart factories have been proposed in literature. This paper aims at presenting an overview of these models in order to find the gaps in the research about the Industry 4.0 concepts. In particular, the business transformation required to evolve the traditional manufacturing systems into Industry 4.0–ready ones is discussed. Finally, future research topics are proposed.

Keywords: Assembly and disassembly, Intelligent manufacturing systems, Flexible and reconfigurable manufacturing systems
Abstract: Industry 4.0 creates what has been called a "smart factory". The possibility to a wide data collection from industrial processes makes possible to act smart action related to the system changes. Cyber-Physical Systems monitor physical processes and make possible decentralized decisions or even, in the last stage, a self-smart system adaptation. Thanks to the connectivity of the different production resources (machines, work stations, etc.) it is possible to generate information, to share and provide it and finally to create a smart system where is possible a predictive and automated decision making process with potential self reconfiguration of the production system. As consequence Industry 4.0 could strongly impact on the actual assembly paradigms, even in the cases where the human factor in prevailing, as manual assembly. This paper aims to investigate how these transformations are expected to occur. A future state map of the assembly paradigms as effect of the integration with the Industry 4.0 principles is presented. Moreover a general framework to implement the Industry 4.0 principles into existing assembly systems is provided, highlighting how the final steps of implementation represented by the Operator Support Systems (OSS) and Self-Adapting Smart assembly System (SASS) are crucial. A detailed report of a real case study derived by an Italian worldwide industrial refrigerator manufacturer represents a further element of interest of the research and demonstrates the practical implication of the study.

Keywords: Manufacturing plant control, Process supervision
Abstract: Complex mineral raw materials processing is an essential operation in modern industry. Innovative technologies help in constant improving of the efficiency and performance. The paper presents a design of electromagnetic mill, its technological structure and control system. In comparison to conventional solutions, the electromagnetic mill is a fully automated system. It operates in more effective way. The proper selection of the physical and process parameters of the system is crucial for efficiency of the process. The device stands out because of its low energy consumption. The air stream ratio model has been presented for the dry grinding and classification unit with the electromagnetic mill. The concept of the grinding system is described along with indirect measurement methodology. Supervisory control algorithm based on the model structure is derived.

Keywords: Data mining and multivariate statistics
Abstract: When considering data-driven modelling, uncertainties, errors and inconsistencies in the data can more often than not lead to sub-optimal predictions. A new framework based on rough sets theory is proposed and applied to an industrial data set obtained from a Charpy impact energy test for alloy steels. The inconsistent/consistent data sets are then used to train a series of artificial neural networks (ANN) for Charpy impact energy prediction for alloy steels. A k-nearest neighbor is used to classify the data points; if an object is classified as consistent, the ANN trained with the consistent data set provides a single prediction while if the object is classified as inconsistent, several ANN trained with different sets of inconsistent data are used to provide an interval prediction. Experimental results show an improvement in the consistent data set compared with a benchmark model. Also, the interval prediction provided by the various ANNs in the inconsistent data set represents a better alternative to the single point prediction results.

Keywords: Neural fuzzy modelling and control, Neural networks in process control
Abstract: For constructing a span-lateral inhibition neural network (S-LINN) with optimal architecture and parameters for actual application, an improved self-organizing optimization approach is proposed in this paper to tune the architecture and parameters simultaneously. This self-organization pruning algorithm can prune insignificant hidden neurons automatically through building a modified significance index function to evaluate the significance of hidden neurons. A preprocessing training of the initial network with assumed redundant hidden neurons will be allowed in the tuning process. A subsequent learning after the self-organization pruning process is also implemented to optimize the parameters of pruned network. The proposed self-organizing approach has been tested on nonlinear dynamic system identification benchmark problem. Simulation results demonstrate that the proposed method has good exploration and exploitation capabilities in terms of searching the optimal structure and parameters for S-LINN.

Keywords: Process optimisation, Identification and modelling
Abstract: Most optimization algorithms suppose that there exist explicit evaluation functions to evaluate the candidate solutions obtained during the optimization process. However, in actual industrial processes, it is usually very difficult to build up precise mathematical models to describe complex industrial processes due to the lack of clear mechanisms. Instead, only some noisy historical data can be used, and optimization of such problem is often known as data-driven optimization. The optimization of complex beneficiation operational process is a typical data-driven optimization problem. To solve this problem, an evolutionary algorithm assisted by Gaussian process model is proposed in this paper. To be specific, a low-order neural network model is constructed by using the data collected from mineral processing factory as real objective function, and a Gaussian process model is developed as a surrogate to reduce the number of real function evaluations. We test the new method on a series of multi-objective test instances against two other algorithms. The experimental results indicate that the proposed method has the ability to achieve significant improvement at the limited budget of real function evaluations. In addition, the proposed algorithm is also successfully applied to the optimization of complex beneficiation operational process.

Keywords: Neural networks in process control, Neural fuzzy modelling and control, Process optimisation
Abstract: Iron making in blast furnace is one of the most complicated industrial processes, especially in its dynamics, inertial properties and multi-scale availabilities. Over the years, researchers have been using silicon content to judge the temperature and the conditions within the blast furnace due to the complexity in measuring the actual status that results from extreme temperatures and intricate environment. Addressing these limitations, a sliding-window Takagi-Sugeno fuzzy neural network(SW-TS FNN) model is proposed to predict the silicon content in hot metal. Through the sliding of a proper width of the sliding-window, the train data for T-S fuzzy neural network(FNN) model can be updated at desired time increments, giving the latest prediction of silicon content. Compared to a simple T-S FNN model on the prediction of silicon content, this SW-TS FNN model shows great improvement at hit rate and mean-square error.

Keywords: Advanced process control, Process optimisation, Monitoring of product quality and control performance
Abstract: Fused magnesia smelting for fused magnesium furnace (FMF) is an energy intensive process with high temperature and comprehensive complexities. Its operational index namely energy consumption per ton (ECPT) is defined as the consumed electrical energy per ton of acceptable quality and is difficult to measure online. Moreover, the dynamics of ECPT cannot be precisely modelled mathematically. The model parameters of the three-phase currents of the electrodes such as the molten pool level, its variation rate and resistance are uncertain and nonlinear functions of the changes in both the smelting process and the raw materials composition. In this paper, an integrated optimal operational control algorithm is proposed and is composed of a current set-point control, a current switching control and a self-optimized tuning mechanism. The tight conjoining of and coordination between the computational resources including the integrated optimal operational control, embedded software, industrial cloud, wireless communication and the physical resources of FMF constitutes a cyber-physical system (CPS) based embedded optimal operational control system. Successful application of this system has been made for a production line with ten fused magnesium furnaces in a factory in China, leading to a significant reduced ECPT.

Keywords: Trajectory Tracking and Path Following, Cooperative navigation, Multi-vehicle systems
Abstract: We introduce a new way to look at the combined lateral and longitudinal control problem for platooning vehicles by studying these problems separately. The lateral control problem is approached as a path following problem in the spatial domain: based on the path of the preceding vehicle we determine a path for the following vehicle which converges to the given path of its predecessor. In particular if the following vehicle happens to be on the path of its predecessor, the generated path of the follower equals the path of its predecessor. This approach not only overcomes the problem of corner cutting, but also achieves appropriate following behavior in case of large initial errors. As a by-product of solving the lateral control problem, we obtain a mapping from the path of the follower to the path of its predecessor. Using this mapping we can consider the longitudinal control problem as controlling two points on the same path towards a required inter-vehicle distance, which is comparable to CACC, i.e., the problem of controlling two points on a straight line towards a required inter-vehicle distance. We illustrate our approach by means of simulation.

Keywords: Multi-vehicle systems, Autonomous Vehicles, Decentralized Control and Systems
Abstract: Nowadays, there is an increasing societal demand for smart mobility solutions which can increase the throughput, comfort, and safety of driving on a road. As one of these solutions, vehicle following technologies like cooperative adaptive cruise control (CACC) are introduced. with the help of vehicle-to-vehicle (V2V) communication, CACC can reduce the safe minimal inter-vehicular distance. Additionally, sharing the vehicle intentions through V2V, enables multi-vehicle maneuvers to be cooperatively executable. In this paper, using the concept of artificial potential fields, a longitudinal control scheme for performing a cooperative merging maneuver will be suggested. Artificial potential fields are powerful enough for modeling and solving such a complex problem, where in addition to vehicle following other objectives such as merging should be addressed. The designed algorithms for vehicle cooperative maneuvering are verified through simulations.

Keywords: Multi-vehicle systems
Abstract: In this paper we present a method of bidirectional control of a vehicle string that achieves l2 weak string stability. Previous work required short-range communications between vehicles within the string. Here, we utilise some recent results on integral action to remove the requirement of communications altogether.

Keywords: Multi-vehicle systems, Mission planning and decision making, Mission control and operations
Abstract: In the aftermath of natural disasters, optimal evacuation planning plays a crucial role in saving people's lives. In this paper, we propose a mathematical model for optimal planning for helicopters with different capabilities to save victims with different urgency levels. The model is formulated as an optimization problem whose objective is maximizing the number of evacuees under operational constraints such as helicopter's capacity and endurance, and different urgency levels of victims. The proposed model is validated with an illustrative numerical example based on real-world data.

Keywords: Multi-vehicle systems, Mission planning and decision making
Abstract: The multiplayer reach-avoid game plays an important role in the applications involving cooperative agents. Analyzing the defendable region for the multiplayer reach-avoid game is the prerequisite for designing the target assignment algorithms and the control laws for both sides of the teams. Since the attacker-defender velocity ratio could be different in different application scenarios, this paper studies the defendable region for the multiplayer reach-avoid game with free attacker-defender velocity ratio. That is to say, the velocity of attacker might be less than, equal to or greater than the velocity of the defender. In order to simplify the problem, the two-dimension one defender versus one attacker issue is first dealt with. Based on the attacker-defender moving time ratio, the concept of the breakthrough point is proposed in this paper. Then, the defendable region of the one versus one reach-avoid game is analyzed based on the concept of breakthrough point. And then the defendable region of the multiplayer reach-avoid game is considered and the defendable regions are classified as strongly defendable regions and weakly defendable regions according to whether the target assignment algorithm is necessary for the defenders. Finally, we illustrate our method by some simulation results.

Keywords: Multi-vehicle systems, Sensor integration and perception, Navigation, Guidance and Control
Abstract: The multiple kill vehicle (MKV) interceptor consists of a carrier vehicle and some small kill vehicles which can intercept multiple targets independently. According to the mission of midcourse interception for multiple warheads of long-range ballistic missile, this paper focused on the design and optimization of cooperative detection and guidance strategy. Based on multi-sensors fusion technology, the method of high-accuracy position measurement and view expansion of infrared sensors are proposed. The terminal guidance law is analyzed based on the data obtained by the methods. A digital real-time simulation system is developed to verify the feasibility of the method.

Keywords: Decision support and control, Healthcare management, disease control, critical care
Abstract: As the number of devices connected to the Internet is growing, the epidemics of malware spreading imposes a serious cyber security problem. It is common that there exist multiple types of malware infecting a network of devices. Periodically scheduled patching is a common way to protect the devices and thwart the malware spreading over a large population of devices. In this paper, we study the heterogeneous SIR model where two types of malware spread over the network and formulate an impulse optimal control problem to describe the optimal strategy of periodic patching that happens at discrete points of time. We obtain the structure of optimal impulse controls and consider the hybrid case in which we combine the discrete impulses and the continuous components of the control. Numerical simulations are used to corroborate the theoretical results.

Keywords: Chronic care and/or diabetes, Dynamics and control, Artificial pancreas or organs
Abstract: Recent results on cardinality constrained optimisation are used to obtain an optimised Multiple Daily Injection (MDI) insulin regimen for Type 1 Diabetes patients. The optimisation is implemented in a rolling horizon fashion to account for unannounced events. A blood glucose model is described and an algorithm developed to illustrate the aforementioned strategy. The impact of varying the number of allowable injections in a given day is also studied. Real patient data is used to obtain the models and simulation results are included.

Keywords: Physiological Model, Dynamics and control, Modeling and identification
Abstract: Medical therapy devices need to interact with the patient to achieve a desired clinical result. Therefore, the patient is part of the final therapy system. But during the development and validation phase of medical devices the patient can not be involved in the design process. A replacement for the patient, or rather the body part which interacts with the therapy device, is needed. Simulators can be used to solve this problem. This contribution introduces the concept of a Hardware-In-The-Loop (HIL) simulator which is designed to be usable in a wide range of applications in the field of medical technology. HIL simulators consist of a software model of the simulated process and a physical interface to the therapy device. Actuators are used to influence the states of the physical interface and sensors measure the interaction between the HIL simulator and the therapy device. As a first application, the process of a minimally invasive surgery (MIS) was chosen. Insight to the process of the MIS was elaborated at the example of an arthroscopy. A software model of the knee, representing the operation area, was developed. A prototype was designed and different types of actuators were implemented. An over-actuated design was chosen to ensure a high flexibility. Control oriented models of the used actuators were derived. Finally, different actuator control strategies were developed, implemented and validated successfully.

Keywords: Biomedical and medical image processing and systems, Medical imaging and processing, Biomedical system modeling, simulation and visualization
Abstract: The determination of the heart bounding box is a key task that can be used in several applications. Balanced steady-state free precession sequence without contrast produces artifacts originated from the blood flow. For this reason, very few research studies using this type of MR sequence have been conducted. However, this drawback can be used to help in the determination of the heart bounding box. The proposed algorithm is very simple, and averagely determines the heart bounding box in the entire MR temporal sequence of images. After that, it is necessary to adjust the vertical position in each image. The adjustment is a consequence of the diaphragmatic movement. The proposed algorithm is evaluated with several sets of MR temporal sequences without contrast.

Keywords: Model formulation, experiment design, Bio-signals analysis and interpretation, Quantification of physiological parameters for diagnosis and treatment assessment
Abstract: Presented study deals with the formulation of a model of the postural system behavior in the form of transfer function derived from Development Statokinesigram Trajectory (DST). As compared with statokinesigram, a DST shows a minimal signs of chaotic behavior while it takes anteroposterior and mediolateral postural sway direction into account together. Proposed method provides an alternative approach to established methodology based on the Center of Pressure (CoP) in evaluating the autoregulation control process in humans. For demonstration of DST application, CoP signals of young and healthy participants without and with bilateral vibration stimulus located on Achilles tendons were registered. Data analysis involves DST construction and numerical parameter estimation of the proposed model. Quadratic criterion showed that there is a disproportion between actual and mean DST time profile that was proposed as a sensitive parameter of posture control quality in all tested subjects in quiet stance condition.

Keywords: Control of physiological and clinical variables, Pharmacokinetics and drug delivery, Physiological Model
Abstract: The input of physiological control systems is positive in almost every case (i.e. it is the concentration of a drug), thus the application of controllers which guarantee positive input is crucial. However, most of the controller design methods can not incorporate the constraint that the input has to be positive. We propose a method based on the dynamic extension of the system that guarantees that the output of the controller is always positive, and design nonlinear controller and observer for the extended system. The extended system is linearized using exact linearization, and path tracking control law is applied to ensure the desired tumor volume regression. The output of the resulting controller is always positive, and achieves the desired tumor regression with low control inputs that are desirable in physiological point of view. The results show that the proposed dynamical extension can be sufficiently used along with exact linearization to control nonlinear dynamic systems with positive inputs.

Keywords: Artificial pancreas or organs
Abstract: Clinical trials have demonstrated that zone model predictive control is an effective closed-loop blood glucose regulation method for people with type 1 diabetes (T1D). This paper presents a universal model-free optimization method to seek an optimal zone for T1D patients individually. A clinical glycemic risk index named relative regularized glycemic penalty index (rrGPI) is used as the cost function. The proposed method is based on extremum seeking control that uses only the rrGPI index, calculated from measurements by a continuous glucose monitor, to update a controller's blood glucose target zone's upper bound and lower bound simultaneously. The method proposed uses a decaying feedback gain and a vanishing dither signal to improve the extremum seeking controller's robustness against various uncertainties. In silico trials suggest that the proposed method is able to converge to the personalized optimal zone in less than a week of adaptation. In a 30-day in silico trial, the time spent in the range [70,180] mg/dL is increased by about 3% and 2% for unannounced 60 gCHO (grams of carbohydrates) and 90 gCHO meals, respectively, compared to the zone [80,140] mg/dL employed in the authors' current zone controller.

Keywords: Biomedical system modeling, simulation and visualization, Control of physiological and clinical variables, Pharmacokinetics and drug delivery
Abstract: Many regulatory loops in drug delivery systems for depth of anaesthesia optimization problem consider only the effect of the controller output on the patient pharmacokinetic and pharmacodynamic response. In reality, these drug assist devices are over-ruled by the anaesthesiologist for setpoint changes, bolus intake and additional disturbances from the surgical team. Additionally, inter-patient variability imposes variations in the dynamic response and often intra-patient variability is also present. All these interactions between the actors of such complex process are important when individualised treatment is envisaged for optimal patient well-being and healthcare. This paper introduces for the first time a study on the effect of both controller and anaesthesiologist in the loop for hypnosis regulation. Among the many control loops, model based predictive control is closest to mimic the anticipatory action of the anaesthesiologist in real life and can actively deal with issues as time lags, delays, constraints, etc. This control algorithm is here combined with the action of the anaesthesiologist. A disturbance signal to mimic surgical excitation has been introduced and a database of 25 patients has been derived from clinical insight. The results given in this paper reveal the antagonist effect in closed loop of the intervention from the anaesthesiologist when additional bolus intake is present. This observation explains induced dynamics in the closed loop observed in clinical trials and may be used as a starting point for next step in developing tools for improved assistance in clinical care.

Keywords: Biomedical system modeling, simulation and visualization, Chronic care and/or diabetes, Physiological Model
Abstract: The primary goal of this paper is to predict fasting glucose levels for people with type 2 diabetes (T2D) in long-acting insulin treatment. The paper presents a model for simulating insulin-glucose dynamics in T2D patients. The model combines a physiological model of type 1 diabetes (T1D) and an endogenous insulin production model in T2D. We include a review of the sources of variance in fasting glucose values in long-acting insulin treatment, with respect to dose guidance algorithms. We use the model to simulate fasting glucose levels in T2D long-acting insulin treatment and compare the results with clinical trial results where a dose guidance algorithm was used. We investigate sources of variance and through simulations evaluate the contribution of adherence to variance and dose guidance quality. The results suggest that the model for simulation of T2D patients is sufficient for simulating fasting glucose levels during titration in a clinical trial. Adherence to insulin injections plays an important role in describing the variance in fasting glucose. For adherence levels 100%, 70% and 50%, the coefficient of variation of simulated fasting glucose levels were similar to the observed variances in insulin treatment. The dose guidance algorithm suggested too large doses in 0.0%, 5.3% and 24.4% of the cases, respectively. Adherence to treatment is an important source of variance in long-acting insulin titration.

Keywords: Cellular, metabolic, cardiovascular, pulmonary, neuro-systems, Modeling and identification of environmental systems, Dynamics and control
Abstract: Gene regulation is crucial for adaptation and biological signals processing. It’s known that the process of gene expression involves the unavoidable interaction with the noise. Which is particularized by two sources: extrinsic and intrinsic. To know the impact of each source, we implemented an in silico plasmid with two identical copies of a gene. Quantifying the noise components through statistical differences of the time series solved through Gillespie algorithm. The model predicted that the degradations rates are crucial to control the extrinsic noise perception in constitutive gene expression.

Keywords: Control of physiological and clinical variables, Biomedical system modeling, simulation and visualization, Pharmacokinetics and drug delivery
Abstract: Optimal and safe control of drug delivery systems with continuous infusion protocol is of key importance to avoid over- or under-dosing of the patient. By implementing close-loops one is able to optimize the amount of drug given to the patient. In this paper a robust control methodology is presented. Emerging tools from fractional calculus have been considered and a fractional order PI controller for drug dosing during hypnosis has been designed. The controller has been evaluated on an artificial data set of 24 patients and the results indicate that such a control strategy is robust to uncertainty stemming from the inter- and intra-patient variability.

Keywords: Cellular, metabolic, cardiovascular, pulmonary, neuro-systems, Biomedical system modeling, simulation and visualization, Bio-signals analysis and interpretation
Abstract: In this paper we aim to study the global stability of a coupled model of healthy and cancerous cells dynamics in healthy situation of Acute Myeloid Leukemia. We also clarify the effect of interconnection between healthy and cancerous cells dynamics on the global stability. The interconnected model is obtained by transforming the PDE-based model into a nonlinear distributed delay system. Using Lyapunov approach, we derive necessary and sufficient conditions for global stability for a selected equilibrium point of particular interest (healthy situation). Simulations are conducted to illustrate the obtained results.

Keywords: Rehabilitation engineering and healthcare delivery
Abstract: Millions of dollars worldwide are spent on electrodes for Functional Electrical Stimulation (FES) every year. With aging populations the increasing costs faced by healthcare providers are of significant concern. Conventional electrodes used for FES are of limited use and deteriorate with use. Washable E-textile electrodes have recently been used successfully for monitoring applications such as electromyography (EMG). E-textile electrodes could potentially reduce the costs faced by healthcare providers. However limited investigation has been conducted into the use of e-textile electrodes for actuation applications such as FES. This paper compares the effectiveness of e-textile electrodes with conventional hydrogel electrodes at a range of stimulation parameters. The electrodes performed similarly under variation of frequency (20-50 Hz) and ramp rate parameters (2-10 V/s). The e-textiles produced a stronger response at a lower voltage than the hydrogels (3-4 V difference for a stimulation intensity voltage range of 8-22 V), however they must be wet to work which may be a barrier to their uptake.

Keywords: Model formulation, experiment design, Identification and validation
Abstract: This paper presents the design of system identification experiments for physical activity behavioral interventions, utilizing a control-relevant identification test monitoring approach. The goal is to find, in real-time, the shortest possible experiment with sufficient information for identification meeting robust performance conditions. The procedure considers multisinusoidal periodic inputs, and allows modifications on the amplitude and harmonic related frequency content of input signals after each periodic evaluation cycle. The local polynomial method (LPM) is utilized for the computation of transfer function and estimation uncertainties. An improved stopping criterion is formulated relying on robust performance metrics. The procedure is tested through a simulation study relying on a hypothetical system based on a real behavioral intervention.