Keywords: Water quality and quantity management
Abstract: Planet earth is endowed with plenty of water, but as all the World Water Reports make abundantly clear, that water is often poorly managed just because of its accepted abundance. The World Bank’s 2030 Water Resources Group, in its inaugural report goes as far as stating that “we are managing our way to water scarcity”. It predicts that by 2030 a significant and increasing portion of the world population will struggle with water shortages over extended periods of time in almost every year. This is further underscored by the observation that the majority of the main river basins already suffer from “over” exploitation. Climate change, population growth and an increasingly middle class life style are the main drivers putting pressure on water use. In this presentation, we describe a systems engineering approach for water management, and articulate a number of value propositions for significant investments in improved water management and control as well as automation. The main value propositions are derived from an economic productivity point of view and/or a social licence to use water as well as providing economically quantified options. Although, as the European Committee on Climate Change has pointed out in early 2017, water access under climate change should also provide a significant incentive for much needed investment.

Keywords: Power systems stability, Control of renewable energy resources, Smart grids
Abstract: In electricity distribution networks, the increasing penetration of renewable energy generation necessitates faster and more sophisticated voltage controls. Unfortunately, recent research shows that local voltage control fails in achieving the desired regulation, unless there is some communication between the controllers. However, the communication infrastructure for distribution systems are less reliable and less ubiquitous as compared to that for the bulk transmission system. In this paper, we design distributed voltage control that use limited communication. That is, only neighboring buses need to communicate few bits between each other before each control step. We investigate how these controllers can achieve the desired asymptotic behavior of the voltage regulation and we provide upper bounds on the number of bits that are needed to ensure a predefined accuracy of the regulation. Finally, we illustrate the results by numerical simulations.

Keywords: Power systems stability, Smart grids, Application of power electronics
Abstract: Currently, there are an increasing number of power electronics converters in electrical grids performing the most diverse tasks but, most of them, work as constant power loads (CPLs). This work presents a sufficient condition for the local stability of dc linear time-invariant circuits with constant power loads for all the possible equilibria (depending on the drained power) of the systems. The condition is shown as a method with successive steps that should be met. Its main step is expressed as a linear matrix inequality test which is important for easiness of verification reasons. The method is illustrated with two examples: a single-port RLC circuit connected to a CPL and a two-port linear dc circuit connected to two CPLs.

Keywords: Power systems stability, Application of power electronics, Modeling and simulation of power systems
Abstract: In a VSC-HVDC system, when the input power to the converter is controlled to a positive constant, the converter acts a differential negative resistance -P0/i0^2 which may cause instability in case of a connected weak AC-grid. In order to analyze the robust stability of such a system, a modified mixed small gain and passivity theorem is proposed, which shows that the negative feedback interconnection of two linear time invariant stable subsystems (converter admittance and AC-grid impedance) is input to output stable if in certain frequency bands, both subsystems are passive and at least one of them is strictly passive; at the remaining frequency bands, the loop gain is to be strictly less than one. Under different AC-grid topologies, the stability sufficient conditions with respect to the AC-grid inductance are studied. It shows that for AC-grid consists of series RgLg-circuit, an introduced series capacitor can improve the system robustness but not for the shunt capacitor.

Keywords: Optimal operation and control of power systems
Abstract: This paper considers the problem of power balancing in a DC microgrid. A PH (port Hamiltonian) formalism is used to describe the system components and interconnections. Energy and power conservation are kept for the discretized model. An economic model predictive controller is used for scheduling the microgrid power management. The proposed approach is validated through simulation results on a particular DC microgrid elevator system.

Keywords: Optimal operation and control of power systems, Smart grids, Control of renewable energy resources
Abstract: This paper studies the optimal power flow problem for resistive DC networks. The gradient method algorithm is written in a port-Hamiltonian form and the stability of the resulting dynamics is studied. Stability conditions are provided for general cyclic networks and a solution, when these conditions fail, is proposed. In addition, the results are exemplified by means of numerical simulations.

Keywords: Modeling and simulation of power systems, Model predictive and optimization-based control, Advanced control technology
Abstract: This work presents a process supervision and advanced control structure, based on Model Predictive Control (MPC) coupled with disturbance estimation techniques and a finite-state machine decision system, responsible for setting energy productions set-points. This control scheme is applied to energy generation optimization in a sugar cane power plant, with non-dispatchable renewable sources, such as photovoltaic and wind power generation, as well as dispatchable sources, as biomass. The energy plant is bound to produce steam in different pressures, cold water and, imperiously, has to produce and maintain an amount of electric power throughout each month, defined by contract rules with a local distribution network operator (DNO). The proposed predictive control structure uses feedforward compensation of estimated future disturbances, obtained by the Double Exponential Smoothing (DES) method. The control algorithm has the task of performing the management of which energy system to use, maximize the use of the renewable energy sources, manage the use of energy storage units and optimize energy generation due to contract rules, while aiming to maximize economic profits. Through simulation, the proposed system is compared to a MPC structure, with standard techniques, and shows improved behavior.

Keywords: Application of power electronics, Control system design
Abstract: This paper presents a novel fast learning neural network for the estimation of load torque in PMDC motor. The control objective of angular velocity trajectory tracking is achieved by designing a controller for cascaded Buck converter PMDC motor system by utilizing an adaptive backstepping methodology augmented with a new Type-II Chebyshev neural network (CNN). The online learning laws for the neural network are developed, satisfying overall closed loop system stability using Lyapunov stability criterion. A rigorous stability analysis has been provided. Performance of the proposed control method is validated on a digital platform using dSPACE Control Desk DS1103 set-up with TM320F240 Digital Signal Processor. The dynamic response of Buck converter driven PMDC motor is examined for settling time, peak undershoot and overshoot guaranteeing the transient performance under conventional adaptive backstepping control and Type-I CNN based adaptive backstepping control techniques. Further, such results are compared with those obtained using the proposed method under start-up, wide range variations in load torque and reference trajectory.

Keywords: Control system design, Optimal operation and control of power systems
Abstract: This paper compares three methods for control of the superheat in a refrigeration system. A traditional gain scheduled PI-based controller, a predictive functional controller (PFC) and a predictive functional controller with a neural network model (PFCNN). The aim is to investigate the performance of the three controllers with respect to disturbance rejection measured both at the superheat deviation from the reference and the actuation of the expansion valve. The controllers are designed and tested on a laboratory set-up. The performance of the controllers turns out to be similar and distinguish between the concepts must be based on other parameters like tuning and demands for computational power.

Keywords: Smart grids, Constraint and security monitoring and control, Instrumentation and control systems
Abstract: Accurate real-time estimation plays an important role in smart grids. Although various filtering algorithms, such as unscented Kalman filter (UKF), have been proposed for phasor measurement unit (PMU), they occupy huge communication bandwidth without specific concern, which puts heavy burden on the communication network especially when wireless communications are applied. In order to relief this burden, the event-based cubature Kalman filter (EBCKF) is proposed based on event-based schemes in this paper. The event-based strategy guarantees that only the observations containing innovational information are transmitted, and cubature Kalman filter (CKF), which is based on the spherical cubature integration, provides more accurate estimation than UKF. Besides, the design-oriented communication rate is provided corresponding to the event-based strategy design. The proposed filter can effectively reduce the communication rate while ensuring the accuracy of filtering. Finally, the standard IEEE 39-bus system is utilized to verify the feasibility and performance of the proposed method.

Keywords: Modeling and simulation of power systems, Real time simulation and dispatching, Smart grids
Abstract: Contingency analysis is a critical function widely used in energy management systems to assess the impact of power system component failures. Its outputs are important for power system operation for improved situational awareness, power system planning studies, and power market operations. With the increased complexity of power system modeling and simulation caused by increased energy production and demand, the penetration of renewable energy and fast deployment of smart grid devices, and the trend of operating grids closer to their capacity for better efficiency, more and more contingencies must be executed and analyzed quickly in order to ensure grid reliability and accuracy for the power market. Currently, many researchers have proposed different techniques to accelerate the computational speed of contingency analysis, but not much work has been published on how to post-process the large amount of contingency outputs quickly. This paper proposes a parallel post-processing function that can analyze contingency analysis outputs faster and display them in a web-based visualization tool to help power engineers improve their work efficiency by fast information digestion. Case studies using an ESCA-60 bus system and a WECC planning system are presented to demonstrate the functionality of the parallel post-processing technique and the web-based visualization tool.

Keywords: Optimal operation and control of power systems, Model predictive and optimization-based control, Intelligent control of power systems
Abstract: This paper describes application of a nonlinear model predictive control algorithm to the problem of dynamic reconfiguration of an electrical power distribution system with distributed generation and storage. Power distribution systems usually operate in a radial topology despite being physically built as interconnected meshed networks. The meshed structure of the network allows one to modify the network topology by changing status of the line switches (open/closed). The goal of the control algorithm is to find the optimal radial network topology and the optimal power references for the controllable generators and energy storage units that will minimize cumulative active power losses while satisfying all system constraints. Validation of the developed algorithm is conducted on a case study of a real-life distribution grid in Croatia. The realistic simulations show that large loss reductions are feasible (more than 13%), i.e. that the developed control algorithm can contribute to significant savings for the grid operator.

Keywords: Power systems stability, Smart grids, Modeling and simulation of power systems
Abstract: This paper describes the design strategy and testing results of a control system to improve damping of inter-area oscillations in the western North American Power System (wNAPS) in order to maintain dynamic stability of the grid. Extensive simulation studies and actual test results on the wNAPS demonstrate significant improvements in damping of inter-area oscillations of most concern without reducing damping of peripheral oscillations. The design strategy of the control system features three novel attributes: (1) The feedback law for the control system is constructed using real-time measurements acquired from Phasor Measurement Units (PMUs) located throughout the power grid. (2) Control actuation is delivered by the modulation of real power flow through a High Voltage Direct Current (HVDC) transmission line. (3) A supervisory system, integrated into the control system is in charge of determining damping effectiveness, maintaining failsafe operation, and ensuring that no harm is done to the grid.

Keywords: Smart grids, Constraint and security monitoring and control, Power systems stability
Abstract: Electromechanical wave propagation affects many aspects of power grids, including control and protection. FNET/GridEye, a wide-area distribution-level measurement system, is found to be able to provide unprecedented insight into power system electromechanical wave behavior. This paper discusses the power system electromechanical wave propagation observed in FNET/GridEye and the implementation of algorithms based on wave propagation, including disturbance detection and location, and the development of the propagation speed distribution map.

Keywords: Power systems stability, Control system design, Smart grids
Abstract: A co-design of the implementation platform and control strategies for wide-area power networks is addressed. Limited and shared resources among control and non-control applications introduce delays in transmitted messages. The design is based on a delay-aware architecture and cloud computing has been proposed for damping wide-area oscillations. We accommodate possibly large delays in the network and take into account their values in the designs. Moreover, we design output feedbacks for the cases that some state variables are not accessible. The designs are verified through a simulation on 50-bus Australian model.

Keywords: Control of renewable energy resources, Control system design, Power systems stability
Abstract: This paper deals with the harmonic stability problem in offshore wind farms caused by the interaction of HVDC controller with the resonance frequencies of the grid. Due to the HVDC filters, transformers and long submarine cables wind farms have natural resonances, which are close to the operating frequency of the HVDC converter. Even through the grid resonances exhibit inherent damping, the converter control may amplify the oscillations resulting in harmonic instability. Therefore, the converter control has to be designed properly taking into account all possible operating conditions and grid topologies. In this paper a new damping control approach is presented that is using the dq components of the current for modulating the converter output voltage. The parameters of the damping controller are estimated by the heuristic optimization algorithm MVMO simultaneously for three different disturbance scenarios. Simulation results in the time domain demonstrate the effectiveness of the proposed method.

Keywords: Optimal operation and control of power systems, Intelligent control of power systems, Control system design
Abstract: DC MicroGrids present an increasing interest as they represent an advantageous solution for interconnecting renewable energy sources, storage systems and loads as electric vehicles. A high-level management system able to calculate the optimal reference values for the local controllers of each of the DC MicroGrid interconnected devices is introduced in this paper. Both the changing environmental conditions and the expected load variations are taken into account. The controller considers power balance and the desired voltage level for the DC microgrid. Constraints taking into account the different nature of the storage devices are also considered.

Keywords: Optimal operation and control of power systems, Control of renewable energy resources, Modeling and simulation of power systems
Abstract: This paper presents the design of several reduced order robust control strategies for the air supply of a hybrid fuel cell power generator. The management of the air dynamic entering the fuel cell is assured by the control of the air flow of a compressor. The air supply sub-system is controlled to keep a desired oxygen excess ratio, this allow to improve the fuel cell dynamic performance. Robustness analysis studies are performed, these robust properties are contrasted with classic control strategies, demonstrating the advantage of the multivariable reduced order robust methodologies.

Keywords: Control system design, Optimal operation and control of power systems, Model predictive and optimization-based control
Abstract: This paper deals with an Optimal Iterative Learning Control approach for the anode pressure during the periodic purge processes of a fuel cell system which is developed to a Predictive Control Strategy under consideration of the correct purge volume. Due to accumulation of diffused nitrogen and water condensate inside the anode volume the chemical reaction is restrained. This adverse effect is avoided through the purge process, by which a short-time opening of the exhaust valve forces the nitrogen and water out of the system. Unfortunately, the opening leads to a pressure drop along the anode volume that causes a force to the membrane. To avoid this mechanical stress the control aim is a constant anode pressure during the purge process by supplying additional hydrogen. Therefore, first an Optimal Iterative Learning Control approach is introduced. Due to an influence of the Optimal Iterative Learning Control on the purge volume, a correction of the purge time interval is applied for a constant purge volume. In the last step a Nonlinear Model Predictive Control approach is presented which allows a correction of the purge volume during load changes as well.

Keywords: Optimal operation and control of power systems, Modeling and simulation of power systems, Constraint and security monitoring and control
Abstract: This paper describes the main problems of operating parabolic trough solar fields during days with partial radiation. An optimal control strategy is proposed to solve these problems and it is assessed against a classical one, which uses a feedforward and a PI controller with a fixed set point of oil outlet temperature. Some simulations have been made using MATLAB to demonstrate that using the optimal control strategy better results can be achieved.

Keywords: Optimal operation and control of power systems, Modeling and simulation of power systems, Model predictive and optimization-based control
Abstract: This paper proposes a model-based predictive control (MPC) approach with economic objective function to face the scheduling problem in concentrating solar power (CSP) plants with thermal energy storage (TES) in a day-ahead energy market context. By this approach, the most recent energy prices, weather forecast and the current plant's state can be used by the proposed economic MPC approach to reschedule the generation conveniently from time to time. The proposed approach is applied, in a simulation context, to a 50 MW parabolic trough collector-based CSP with TES under the assumption of perfect price forecast and participation in the Spanish day-ahead energy market. A case study based on a four-month period to test several meteorological conditions is performed. In this study, a complete economic analysis is carried out using actual values of energy price, penalty cost, solar resource data and its forecast. Results show an economic improvement in comparison with a traditional day-ahead scheduling strategy.

Keywords: Modeling and simulation of power systems, Control of renewable energy resources, Optimal operation and control of power systems
Abstract: In this research, a Web application for a simulator of an ocean thermal energy conversion (OTEC) plant with double-stage Rankine cycle is developed. A simple dynamic model of the OTEC plant with double-stage Rankine cycle which consists of steady state calculation of state variables based on mass balance and energy balance, and transient calculation of the heat exchanger dynamics is adopted for construction of the simulator. Performance indexes which are used for effective OTEC operation are also calculated in the simulator.

Keywords: Modeling and simulation of power systems, Constraint and security monitoring and control
Abstract: This article addresses the possible occurrence of hydraulic hammer effects in solar power towers and the limitations they impose on the response bandwidth of the control loop that governs the valve actuating on heating fluid flow. The partial differential equations that model the traveling waves associated to the hammer effect are solved with different methods. A finite dimensional approximation of the exact solution, by a transfer function with a finite number of poles is obtained. It is concluded that, for the speed of response of the temperature controller associated to the valve, there is no danger of inducing hydraulic hammer effects that can damage the equipment.

Keywords: Control system design, Control of renewable energy resources, Modeling and simulation of power systems
Abstract: This research proposes a novel control strategy for the control of ocean thermal energy conversion (OTEC) plant using double-stage Rankine cycle with the variation of warm seawater temperature. The control strategy is derived based on the heat exchange in evaporator and condenser of OTEC plant, and realized by PI control law. The usefulness of the proposed control strategy is conrmed through numerical simulations using a simple dynamic model.

Keywords: Control of renewable energy resources, Power systems stability, Control system design
Abstract: This paper presents a robust feedback model predictive control (MPC) strategy for a sea wave energy converter (WEC). The control objective is to maximise the energy output while reducing the potential risk of device damage subject to sea wave excitation forces as persistent external disturbance. The robust feedback MPC proposed in this paper has the following prominent advantages: (i) the robust feasibility and stability can be guaranteed. (ii) the robust MPC method does not rely on the real time wave prediction. These features can not only reduce the unit cost of energy generation but also increase the reliability of the MPC controller.

Keywords: Control of renewable energy resources, Dynamic interaction of power plants, Real time optimization and control
Abstract: A cooperative extremum seeking control technique is employed to overcome the need for accurate models of aerodynamic wake interactions among wind turbines thereby providing an effective technique for farm wide power capture. In this paper, a time-varying extremum seeking control (TVESC) approach is employed in a distributed fashion to maximize the power production of an array of wind turbines under constant and varying free stream wind speeds and directions. Each wind turbine (identified as an agent) has access to the measurement of its unknown local power generation. The goal is to maximize the unknown total power capture in the wind farm. A cooperative approach is employed where the agents work together to maximize this overall objective function. This approach requires every agent to exchange information with its neighbours over an undirected connected communication network. Each agent is also expected to estimate the mean of the overall cost function and this is achieved using a dynamic average consensus estimator. The dynamics of each agent's cost estimate is parametrized and a parameter estimation routine is used to estimate the unknown gradient information. A distributed extremum seeking controller is designed to ensure that the overall cost is maximized. This problem is tackled via numerical simulations, results are provided to show the effectiveness of this technique.

Keywords: Modeling and simulation of power systems, Optimal operation and control of power systems
Abstract: Multiple-kite Airborne Wind Energy Systems (MAWES) aim to decrease intermittency and cost over conventional wind turbines, while generating more power than other airborne wind energy systems. The purpose of this work is to estimate whether axial and angular induction are relevant phenomena in the modelling of pumping-cycle MAWES with two or more kites. Considering the modelling assumptions, axial induction is a relevant phenomenon and leads to significant changes in design-point, especially with respect to kite mass and secondary tether length. However, angular induction can be neglected in modelling for optimal design and control problems.

Keywords: Optimal operation and control of power systems, Control of renewable energy resources, Process optimisation
Abstract: Wind energy is one of the most progressive types of renewable energies deployed using wind turbines, which convert wind power into electrical energy. Various methodologies and layouts have been proposed to determine the optimal layout (numbers and locations) of wind turbines inside a wind farm to extract maximum energy. However, limited availability of land area has resulted in the construction of these wind farms near to human habitats causing a negative impact on human health. Compared to other factors, noise has become an immense concern for wind farm designers, as it needs to be constrained within the mandatory limits. Using a well-established wake model and ISO-9613-2 noise calculation, this study performs a wind farm layout optimization (WFLO) based on a multi-objective trade-off between minimization of noise propagation and maximization of energy generation. A novel hybrid methodology is proposed as a combination of proposed probabilistic variable decomposed multi-objective evolutionary algorithm (VdRBNSGA-II) and a newly developed gradient-based non-dominated normalized normal constraint (nD-NNC) method. As opposed to previous works, generated non-dominated front provides various alternative energy-noise solutions along with an additional information on turbine layouts, which allows a decision maker to select among different competing solutions based on the existing noise and other regulations.

Keywords: Optimal operation and control of power systems, Smart grids, Modeling and simulation of power systems
Abstract: In this paper, we formulate a problem of power profile market clearing and develop a distributed market clearing scheme with explicit consideration of high battery penetration. The power profile market is a multiperiod electricity market in which each aggregator aims at making the highest profit by transacting a power profile, i.e., a time sequence of energy amounts at several time slots, that is generated by dispatchable power generation as well as the charge and discharge of batteries. It is theoretically shown that the clearing price profile during the time period of interest tends to level off in the high penetration of batteries. This finding enables to develop a distributed market clearing scheme that is implemented as a bidding strategy for the total energy amount during the period followed by a distributed iterative algorithm for profile imbalance minimization. Numerical simulations demonstrate the price leveling-off led by high battery penetration and the efficiency of the proposed distributed scheme.

Keywords: Smart grids, Control system design, Real time simulation and dispatching
Abstract: Demand-side management, involving energy efficiency, demand response, and distributed generation, is an efficient and effective way to improve the security of electricity via changing the profile of power demand, but portfolio management of these resources still lack systematic and comprehensive investigation. In this paper, a optimization-based framework of demand-side management is proposed, with resource optimization at the bottom level and portfolio optimization at the top level. Resource optimization is modeled in terms of energy efficiency, demand response, and distributed generation. Portfolio optimization is also generalized into four categories, and for each category an instance of application is studied. Reported results shown that the portfolio approach is essential to reach full potential of demand-side resources and to achieve the best energy and cost savings.

Keywords: Distribution automation
Abstract: Economic dispatch is one of the fundamental problems in the power system research. The existing algorithms are either discrete iterative algorithms or continuous-time dynamical algorithms. By virtue of the hybrid techniques, a hybrid consensus-based algorithm is proposed in this paper, which consists of a finite-time continuous-time algorithm and a discrete-time interacted scheme. Moreover, inspired by the cluster synchronization and the benefit of the projection operator, a supervised strategy is carried out to ensure that each generator unit will work within its power generation constraint. For a better understanding of the supervised hybrid distributed algorithm, the detailed pseudo-code is presented. Finally, a numerical example is given to illustrate the effectiveness of the algorithm.

Keywords: Modeling and simulation of power systems, Optimal operation and control of power systems, Smart grids
Abstract: Battery Energy Storage Systems (BESS) can mitigate effects of intermittent energy production from renewable energy sources and play a critical role in peak shaving and demand charge management. To optimally size the BESS from an economic perspective, the trade-off between BESS investment costs, lifetime, and revenue from utility bill savings along with microgrid ancillary services must be taken into account. The optimal size of a BESS is solved via a stochastic optimization problem considering wholesale market pricing. A stochastic model is used to schedule arbitrage services for energy storage based on the forecasted energy market pricing while accounting for BESS cost trends, the variability of renewable energy resources, and demand prediction. The uniqueness of the approach proposed in this paper lies in the convex optimization programming framework that computes a globally optimal solution to the financial trade-off solution. The approach is illustrated by application to various realistic case studies based on pricing and demand data from the California Independent System Operator (CAISO). The case study results give insight in optimal BESS sizing from a cost perspective, based on both yearly scheduling and daily BESS operation.

Keywords: Power systems stability
Abstract: Control reserves are power generation or consumption entities that ensure balance of supply and demand of electricity in real-time. In many countries, they are procured through a market mechanism in which entities provide bids. The system operator determines the accepted bids based on an optimization algorithm. We develop the Vickrey-Clarke-Groves (VCG) mechanism for these electricity markets. We show that all advantages of the VCG mechanism including incentive compatibility of the equilibria and efficiency of the outcome can be guaranteed in these markets. Furthermore, we derive conditions to ensure collusion and shill bidding are not profitable. Our results are verified with numerical examples.

Keywords: Distribution automation, Smart grids, Control of renewable energy resources
Abstract: Sub-Saharan Africa in recent years have been faced with many power challenges including limited and unreliable power supply, high cost of power supply and underdeveloped power infrastructures with serious negative impacts on the socio-economic development. This paper presents the development of Smart Energy Management Solution (SEMS) for buildings. The work seeks to propose a generic algorithm that supports existing wiring infrastructure of buildings in Africa to provide 24/7 reliable power supply and efficient management of limited power supply in buildings. SEMS focuses on the use of robust, distributed and heterogeneous power sources connected in islanding phenomenon to ensure reliability and also using a dynamic approach with energy efficiency measures to provide a balance between power supply and demand in buildings. A demonstration of SEMS for buildings was performed in MATLAB Simulink using actual data obtained from selected buildings at Kwame Nkrumah University of Science and Technology (KNUST), Ghana. SEMS revealed higher power reliability and efficiency in energy usage.

Keywords: Smart grids, Intelligent control of power systems, Optimal operation and control of power systems
Abstract: This paper proposes a total optimization method of a smart community (SC) by Differential evolutionary particle swarm optimization (DEEPSO). Various sectors of SC models such as an electric utility model, an industry model, and a building model are utilized in this paper. Whole of a SC is optimized in order to minimize energy costs, shift electric power load peak at high load hours, and minimize total CO2 emission of the SC using the models. The simulation results by the proposed method are compared with those by Particle swarm optimization (PSO), Differential Evolution (DE), and Evolutionary particle swarm optimization (EPSO) based methods.

Keywords: Smart grids, Control of renewable energy resources, Optimal operation and control of power systems
Abstract: The supply/demand balance problem plays a pivotal role in the electricity grid, especially when an increasing proportion of power is generated from renewable resources. Enormous supply/demand models have been put forward in order to handle the balance problem in the smart grid, in the presence of high renewable penetration. This has brought an awareness that the flexibilities in the demand can be exploited to alleviate the burden on the supply. In view of this, we apply and study differentiated energy services, which distinguish demands in terms of their available flexibilities. As a starting point, we concentrate on two problems regarding adequacy. On the one hand, we find both numerical and analytical ways to check the adequacy of a supply. On the other hand, we characterize the adequacy gap in the case of an inadequate supply.

Keywords: Modeling and simulation of power systems, Control system design
Abstract: This paper proposes a vehicle-to-grid (V2G) charging framework has been proposed in this paper owing to the optimal charging/discharging characteristics of the electric vehicles'(EVs) batteries with respect to the change in frequency in the grid. Since battery overcharging deteriorates its lifetime, the proposed charging framework ensures virtual tripping of the vehicles from the plug-in charging terminal once it is fully charged. Additionally, a faster DC bus voltage droop controller has been used for the voltage source converter (VSC) instead of the conventional low bandwidth PI controller to overcome the sluggish response. A negative feedforward signal alongwith the droop controller has been used to achieve DC voltage regulation. On basis of extensive simulation carried out in MATLAB/SIMULINK, the results demonstrate the effective performance of the V2G charging strategy as well as the faster performance of droop controller over PI controller.

Keywords: Instrumentation and control systems, Control of renewable energy resources
Abstract: One way to optimize their operation of Concentrated Solar Power (CSP) plants is to obtain accurate short-term forecasts of Direct Normal Irradiance (DNI). To do so, the use of sky imagers is developing. They should provide high-quality images, especially in the circumsolar area, where pixels are usually saturated. To reduce the saturated area around the Sun, High Dynamic Range (HDR) imaging is needed. This paper describes the steps required to obtain HDR images from a sequence of low dynamic range images acquired using a sky imager. Since the HDR image will be used to study the sky radiance and the circumsolar area, a particular care is taken to maintain a linear sensor's response throughout the process.

Keywords: Analysis and control in deregulated power systems, Modeling and simulation of power systems, Optimal operation and control of power systems
Abstract: We present a theoretical analysis of virtual bidding in a stylized model of a single bus, two-settlement electricity market. North-American ISOs typically take a conservative approach to uncertainty, scheduling supply myopically in day-ahead (DA) markets to meet expected demand, neglecting the subsequent cost of recourse required to correct imbalances in the real-time (RT) market. This can result in generation costs that far exceed the minimum expected cost of supply. We explore the idea that virtual bidding can mitigate this excess cost incurred by myopic scheduling on the part of the ISO. Adopting a game-theoretic model of virtual bidding, we show that as the number of virtual bidders increases, the equilibrium market outcome tends to the socially optimal DA schedule, and prices converge between the DA and RT markets. We additionally analyze the effects of virtual bidding on social welfare and the variance of the price spread. Finally, we establish a repeated game formulation of virtual bidding, and investigate simple learning strategies for virtual bidders that guarantee convergence to the Nash equilibrium.

Keywords: Intelligent control of power systems, Distribution automation, Control of distributed systems
Abstract: In this paper we present the application of the interior-point decomposition (IPD) method, which was originally formulated for stochastic programming, to optimization problems involving multiple agents that are coupled through constraints and objectives. IPD eliminates the need to communicate local constraints and cost functions for all variables that relate to internal dynamics and objectives of the agents. Instead, by using embedded barrier functions, the problem is solved in the space of coupling variables, which are in general much lower in dimension compared to internal variables of individual agents. Therefore, IPD contributes to both problem size reduction as well as data hiding. The method is a distributed version of the primal barrier method, with locally and globally feasible iterations and faster convergence compared to first-order distributed optimization methods. Hence, IPD is suitable for early termination in time-critical applications. We illustrate these attractive properties of the IPD method with a distributed Model Predictive Control (MPC) application in the context of smart-grids, where a collection of commercial buildings provide voltage support to a distribution grid operator.

Keywords: Modeling and simulation of power systems, Optimal operation and control of power systems, Process optimisation
Abstract: This study proposes solving the constraint optimal power flow problem (OPF) by using vortex search algorithm (VSA). VSA is inspired by natural vortexes. Piecewise quadratic fuel cost and quadratic cost curve with valve point loadings test cases are solved on IEEE-30 bus test system by taking into consideration the system constraints such as generation limits, voltages at nodes, tap settings. The obtained test results show that VSA gives better results than any other algorithms which are used to solve the OPF problem.

Keywords: Optimal operation and control of power systems, Control of renewable energy resources, Smart grids
Abstract: This paper studies a day-ahead scheduling problem on power generation of thermal power plants and charge/discharge of battery energy storage systems, where the confidence intervals of the prediction of photovoltaic (PV) and demand power are available. This problem is reduced to an interval optimization problem, where parameters having any values in certain intervals are included. We have developed a method for efficiently solving this kind of problems using tools from the interval analysis. However, the class of the problem that we have studied there is limited in the sense that the output capacity constraints of thermal power plants are not taken into account. This paper proposes a new method for efficiently solving a more practical scheduling problem with the capacity constraints of thermal power plants added. To use our method, we need to analyze a matrix corresponding to a Jacobian of a solution to an optimization with respect to parameters on demand deviation, which is too complex to analyze in general. The key to overcome this difficulty is that we introduce a virtual thermal power plant whose output capacity constraint is not imposed. By using this idea, we can transform the above matrix into an appropriate matrix that is analyzed as easily as possible. The efficiency of the proposed method is shown by numerical simulations on the power system in the Tokyo area.

Keywords: Map building, Localization, Intelligent transportation systems
Abstract: Automated vehicles require robust and precise ego-localization, especially when operating in challenging environments such as urban intersections. One possibility to improve localization accuracy is the utilization of high-definition digital maps combined with on-board sensors capable of recognizing the mapped features. In this paper, a framework for the creation of digital maps based on LiDAR data and for localization using these maps is presented. Graph SLAM is used to calculate optimal maps given LiDAR, IMU, odometry and GNSS data and a particle filter-based localization approach is proposed for localization. The performance of the framework is experimentally demonstrated by conducting mapping and localization tests in an outdoor test environment.

Keywords: Trajectory Tracking and Path Following, Autonomous Vehicles, Control architectures in automotive control
Abstract: Intelligent vehicles have improved their highly and fully automated driving capacities in the last years. Most of the developments are driven by the fast evolution of embedded systems for the acquisition, perception and communication modules. However, the fast growing of the automated vehicle market demands modern tools for validation, integration and testing of these new embedded functionalities, specially related to Advanced Driving Assistance Systems (ADAS). In this paper, a testing methodology for validation of path planning and control algorithms for current and future automated vehicles is presented. A high degree of modularity and adaptability have been considered in the design of the proposed method. It is based on a software tool for vehicle modeling, called Dynacar, which allows a good trajectory definition, cooperative maneuvers interaction and virtual validation. Different types of vehicles, scenarios (i.e.: urban, interurban, highways under different environmental conditions) and controllers can be tested. Moreover, Hardware-In-the-Loop configuration (i.e. electronic control units) can be also tested. Simulation results show a good performance in the implementation and configuration of urban scenarios, using different controllers in the proposed framework.

Keywords: Sensor integration and perception, Sensing, Autonomous Vehicles
Abstract: Multi-sensor object detection and tracking on a highway scene with radar measurements is presented. The estimation algorithm is the random finite set based Bernoulli filter, working in the Bayesian framework. The recursion for calculating the Bayes estimation is implemented as a particle filter. %The sensors are attached to moving platforms, which poses difficulties in the information fusion process. A method is presented for calculating the likelihoods, suitable for particle filtering performed with moving sensors, assuming additive Gaussian measurement noise. In our approach, for calculating the posterior estimate of the object state, the measurement likelihoods are computed in the state space, instead of the measurement space, by mapping each measurement to the global coordinate system. The map consists of a nonlinear and an affine part. While the affine transformation trivially preserves the Gaussian nature, the nonlinear is well-proven to be approximated as affine too. This approach allows the particles to be drawn directly from the state space, hence the evaluation of the measurement model is not needed, which saves computational power.

Keywords: Fault Detection, Diagnosis, Identification, Isolation and Tolerance for Autonomous Vehicles, Autonomous Mobile Robots, Health monitoring and diagnosis
Abstract: Online estimation of control effectiveness in combination with appropriate controller reconfiguration is a promising method for introducing a certain fault-tolerance into a control system. The contribution of this paper is twofold. First, it investigates the observability of the parameters to be determined for a hexacopter; second, a novel approach is introduced which combines moving horizon estimator (MHE) and Lyapunov-based methods. Conventional and proposed estimators are tuned and validated to assess their performance. Ease of implementation along with beneficial error characteristics are the main strength of the presented nonlinear MHE-augmented Lyapunov-based estimator.

Keywords: Autonomous Vehicles, Sensing, Positioning Systems
Abstract: Precise and robust localization is a significant task for autonomous vehicles in complex scenarios. The accurate position of autonomous vehicles is necessary for decision making and path planning. In this paper, a novel method is proposed to precisely locate the autonomous vehicle using a 3D-LIDAR sensor. First, a curb detection algorithm is performed. Next, a beam model is utilized to extract the contour of the multi-frame curbs. Then, the iterative closest point algorithm and two Kalman filters are employed to estimate the position of autonomous vehicles based on the high-precision map. Finally, experimental results demonstrate the accuracy and robustness of the proposed method.

Keywords: Trajectory and Path Planning, Cooperative navigation, Autonomous Vehicles
Abstract: This article discusses the rendezvous maneuver for a fleet of small fixed-wing Unmanned Aerial Vehicles (UAVs). Trajectories have to be generated on-line while avoiding collision with static and dynamic obstacles and minimizing rendezvous time. An approach based on Model Predictive Control (MPC) is investigated which assures that the dynamic constraints of the UAVs are satisfied at every time step. By introducing binary variables, a Mixed Integer Linear Programming (MILP) problem is formulated. Computation time is limited by incorporating the receding horizon technique. A shorter planning horizon strongly reduces computation time, but delays detection of obstacles which can lead to an infeasible path. The result is a robust path planning algorithm that satisfies the imposed constraints. However, further relaxation of the constraints and fine-tuning is necessary to limit complexity.

Keywords: Autonomous Vehicles, Nonlinear and optimal automotive control
Abstract: This paper focuses on high-level control and decision-making for an autonomous car. We develop a hybrid probabilistic model that describes the motion of a car and its surrounding traffic on a two-lane highway/road, where the acceleration of the car and the lane changes serve as control variables. Using approximate dynamic programming (ADP) techniques and an enhanced version of the value iteration algorithm, a control policy is obtained that maximizes the expected time that the car maintains a prescribed minimum (safe) headway. Simulation results for different settings are provided to validate the approach.

Keywords: Autonomous Vehicles, Trajectory Tracking and Path Following, Motion control
Abstract: This paper studies the trajectory tracking problem for the nonlinear model of a scooter and presents a robust H-infinity controller based on measurements of the tracking errors, the roll angle, the yaw angle and the steering angle. The study first introduces the full nonlinear model developed in Autosim which has 12 degrees of freedom. This is far more complex than a simple bicycle model and provides a good description of the scooter. Then a robust H-infinity controller based on the linearization of the nonlinear model is designed. Finally, the effectiveness of the controller is verified by means of two case studies.

Keywords: Trajectory Tracking and Path Following, Decentralized Control and Systems, Autonomous Vehicles
Abstract: This work presents a control strategy for the trajectory tracking problem of an Automated Guided Vehicle (AGV). In contrast to the current methods, this design strategy remains invariant and flexible to arbitrary number of wheels. A three-stage cascade control strategy is proposed in which the control design of the vehicle chassis is separated from the wheel-tire modules. For a given vehicle reference trajectory, the outer controller determines the required forces and moment inputs to the vehicle chassis in a time-receding fashion. At the second stage, the required forces and moment inputs are optimally allocated for each module of wheel and tire. At each wheel-tire module, a nonlinear controller is used to determine the actual control input for the wheel actuators. The performance of the presented control strategy is illustrated through simulation results with a realistic driving scenario for a six-wheeled vehicle. We demonstrate that the proposed controller architecture is configurable for an arbitrary number of wheels and capable of handling large steering angles efficiently.

Keywords: Multi-vehicle systems, Motion control, Autonomous Mobile Robots
Abstract: This paper studies the collision avoidance problem in the time-varying formation tracking control for multi-agent systems. The proposed control strategy is decentralized, since agents have no global knowledge of the goal to achieve, knowing only the position and velocity of some agents. This control strategy allows a set of mobile agents to track a predetermined trajectory while they achieve a time-varying formation. For the collision avoidance, we add a repulsive vector field of the unstable focus type to the time-varying formation tracking control law. A leader-followers scheme is employed, using formation graphs to represent interactions between agents. The results are presented for the front points of differential-drive mobile robots. The theoretical results are verified by numerical simulation.

Keywords: Fault Detection, Diagnosis, Identification, Isolation and Tolerance for Autonomous Vehicles, Safety
Abstract: In order to realize high-speed train bogie’s fault intelligent identification by data driven method, this paper proposes a new fault diagnosis framework. The main idea of the framework is to use features of ensemble empirical mode decomposition entropy, to reduce the feature dimension by Isometric Feature Mapping Manifold Learning, and identify the faults using support vector machine. The proposed method increases the fault detection rate effectively. Experimental results verify that the new method increases the accuracy of fault detection rate of the bogie failure.

Keywords: Trajectory and Path Planning, Navigation, Robot Navigation, Programming and Vision
Abstract: Inspired by the 'fine-to-coarse' way-finding strategy that human utilized in the process of navigation, the paper proposed a fast on-line global path planning algorithm based on regionalized roadmap. First, a regionalized roadmap(RRM) that has a multi-layered structure is proposed for representing environments. Then, the RRM based FTC-A* algorithm is designed to plan an FTC-route(`fine-to-coarse' route) with being fine in vicinity yet coarse at a distance. This algorithm can be applied to on-line global path planning in navigation system of mobile robots or vehicles. Finally, the simulation and physical experiments have been carried out to show the efficacy of the proposed path planning algorithm which can be applied to such occasions as large-scale environment and dynamic changes of the destination.

Keywords: Autonomous Vehicles, Trajectory and Path Planning, Trajectory Tracking and Path Following
Abstract: The goal of this work is to present the particle swarm optimization application for quadrotor attitude and path following control tuning. To perform this task a path following feed-forward plus proportional-derivative control strategy was implemented, using particle swarm for tuning gains, and root mean square error for validating. The basic of quadrotor kinematics and dynamics model will be presented. Path planning will be executed through Euler-Lagrange equations to minimize the snap cost function and guarantee a soft trajectory through a set of intermediary waypoints. The reliability of this approach will be tested through several simulations.

Keywords: Tele-maintenance, Remote servicing, Tele-presence
Abstract: This paper broaches the issue of using tracking algorithm frameworks on mobile devices in order to enable cooperative repair tasks. A local service technician can be supervised remotely by an external expert in the context of telemaintenance. At first we introduce several tracking libraries and test them on low-performance mobile devices. After that we conduct a test run on a large amount of pictures derived from video streams with different characteristics of the application area in order to find the best suited algorithm. At the end we describe the implementation on several mobile devices and delay considerations.

Keywords: Internet of things
Abstract: The paper introduces new generic architecture for Internet of Things (IoT) applications with focus on online experimentation. The proposed architecture was inspired by many common features shared between IoT applications and online laboratories. The architecture follows principles of component based design of software systems, and reusability of components. The main goal of the proposed architecture and its implementations is to simplify and accelerate future development and deployment of new online laboratories and IoT applications as well.

Keywords: Tele-education, Real-time algorithms, scheduling, and programming, Remote servicing
Abstract: This article illustrated the development of a web simulation system based on Matlab distributed parallel cluster. The web simulation system construct a visual and controllable simulation interface using javascript and Asynchronous JavaScript and XML(AJAX) technology. In order to realize the Matlab resources dynamic allocation and management, elastic resources management method has been adopted. To achieve the multi-user concurrent access, the resource management system has been designed by adopting principle of feedback control and principle of Apache Hadoop Yet Another Resource Negotiator(YARN). The feasibility of this system demonstrated through application of simulation and practical use.

Keywords: Remote and distributed control, Tele-robotics, Remote servicing
Abstract: In this paper, the optimal H_{infty} control of a linear time invariant (LTI) Networked Control System (NCS) over a lossy network is analyzed. Specically, we consider a TCP-like data network where packet drops are modelled using a two-state Markov process. By employing the theory of dynamic games, a state-feedback H_{infty} controller is obtained. It is shown that, subjected to a suitable H1 disturbance attenuation level and suitable packet loss probabilities such a controller exists for both finite and infinite horizon cases. We also show the asymptotic stability of the NCS in the mean-square sense. Finally a numerical example is presented to demonstrate our work.

Keywords: Internet of things, Remote and distributed control, Telecommunication-based automation systems
Abstract: For data transmission of industrial applications parallel operations of various wireless communication systems are required in an industrial application environment. For guaranteeing that every application communication requirement of each wireless communication is fulfilled, those systems have to be coexistent between each other. The process ensuring coexistence is called coexistence management process. In current approaches this holistic management is not considered yet. We want to develop an automated coexistence management with control engineering considerations. In this contribution we propose a basic model approach for a coexistence plant. For describing the structure and the time behaviour of a coexistence plant, Petri-net graphs are used. Therefore we split the state space into two description types. The first one describes the transfer of messages and the second one describes the time behaviour of state transition in the coexistence plant. Finally, the derived model on measurements in a 2.4 GHz frequency spectrum within a WiFi test environment is parametrised and validated.

Keywords: Remote and distributed control, Programmable logic controllers
Abstract: Industrial protocols based on Ethernet are common in automation and control systems nowadays. As different protocols claim the advantages over competitors based on their own experimental results, it is necessary to perform independent analyses where the industrial networks are analyzed. In this work, five different tests are perfomred to compare a well known protocol as Ethenet/IP with POWERLINK, a relatively new and open protocol. The aim is to provide results able to point the applications where each of the protocols is best suited. Commercial automation controllers from Omron Electronics and B&R are used. Results show that, for general purpose applications, both protocols provide similar result, is only in case of cyber-attack (identity supplantation) or remote management provides some differences.

Keywords: Quantitative feedback theory, Tracking, Disturbance rejection
Abstract: An innovative approach of gaining an insight into motor skills involved in human body flight is proposed. Body flight is the art of maneuvering during the free fall stage of skydiving, which is a rapidly developing sport. The key idea is creating an autonomous system capable of performing skydiving maneuvers in a virtual way, and turning it into a powerful tool for improving instruction methods. Towards this goal, the Skydiver Simulator is developed, comprising Biomechanical, Aerodynamic, and Kinematic Models, Dynamic Equations of Motion, and a Virtual Reality Environment. A Two-Input-Two-Output controller is designed to track the desired inertial motion, producing commands in terms of limbs movements. The natural kinematic redundancy of the human body is resolved by introducing movement patterns, constructed according to ergonomic considerations and empirical knowledge of how skydiving maneuvers are performed. The controller is designed with the use of Quantitative Feedback Theory, providing robustness for dealing with plant non-linearities and inaccurate execution of the movement patterns. The skydiver simulator allowed reconstruction of many challenging aspects of body-flight observed by practicing skydivers. The virtual skydiver, comprising a controller and a guidance algorithm, was shown to autonomously perform meaningful body flight missions.

Keywords: Robustness analysis, Time-varying systems, Quantized control
Abstract: This paper investigates the robust stability of uncertain discrete-time linear systems with both input and output quantization. Specifically, the output of the plant and the output of the dynamic controller are quantized via two independent static logarithmic quantizers. In fact, there are three blocks of uncertainties under consideration due to the double quantization and uncertain plant. First, a necessary and sufficient condition in terms of LMI is proposed for the quadratic stability of the closed-loop system with double quantization. Second, the necessary and sufficient condition is further improved for the case with uncertainty in the plant. Moreover, it is shown that the proposed condition can be exploited to derive the coarsest logarithmic quantization density under which the uncertain plant can be quadratically stabilized via quantized state feedback. Lastly, a new class of Lyapunov function which depends on the quantization errors in a multilinear way is developed to obtain less conservative results.

Keywords: Robustness analysis, Disturbance rejection (linear case), Optimal control theory
Abstract: We present a simple algorithm for computation of H2-optimal tracking and disturbance rejection controller of discrete-time systems possessing invariant zeros on the unit circle, based on polynomial spectral factorization. We prove that the column degrees of the associated para-hermitian polynomial matrix to be factorized are equal to the plant controllability indices. A numerical/computer simulation example is given.

Keywords: Robust control applications, Disturbance rejection, Probabilistic robustness
Abstract: In this paper, the problem of designing a cyber-resilient controller is studied for a class of nonlinear discrete systems subject to actuator attacks. We develop a coupled control design approach, which incorporates interaction between the intrusion detection system (IDS) and the attacker, with the controller design in the physical layer. In the considered cyber-attack scenario, the attacker attempts to first bypass the IDS to spoof the system actuator and then deteriorate the controller performance in the physical layer. The optimal attack strategy to deceive the IDS is obtained based on a zero-sum game in the detection layer. In the physical layer, it is assumed that the system has a secure compensator along with the actuator to deal with the injected attack values. Therefore, the controller and the compensator are designed based on the results of the game in the detection layer, and stochastic stability analysis and Lyapunov function methods are used to prove boundedness of the system state in the probabilistic sense. Finally, through numerical analysis of a representative example, the proposed design procedure is illustrated and its efficacy in maintaining robust stability of the cyber-physical system under actuator attacks is demonstrated.

Keywords: Robustness analysis, Robust linear matrix inequalities, Uncertainty descriptions
Abstract: In this work, a procedure is presented for performance resilience analysis of continuous-time systems controlled by full-order dynamic feedback compensators. The resilience property is defined in terms of defining maximum perturbations on both the controller and observer gains that will maintain controller and observer eigenvalues in disjoint regions in the complex plane so that the closed loop system will sustain certain performance characteristics. The desired performance is characterized by the response speed and magnitude bounds on the response. Therefore, the intersection of two regions are chosen, vertical strips and sector regions, to guarantee upper and lower bounds on the settling (or response) time and an upper bound on the percent overshoot simultaneously. Maximum allowable gain perturbations are obtained based on the designer’s choices of closed loop eigenvalues and the regions in which eigenvalues remain when the gains are perturbed. The linear matrix inequality technique is used throughout the analysis process. Illustrative examples are included to demonstrate the effectiveness of the proposed methodology.

Keywords: Robustness analysis, Uncertainty descriptions, Linear systems
Abstract: mathcal{H}_{infty}/mu methods are commonly used in Airbus Defence and Space for the design and validation of control solutions. Formulated in a worst-case paradigm, these methods necessarly lead to overly conservative solutions since sized on the extreme cases. However, the acceptance for relaxed control performances requires mastering the risk associated to the detected unlikely events calling for probabilistic performances metrics in the validation process. A probabilistic mu-analysis method is presented in this paper to exhaustively explore the uncertain parametric domain while evaluating the cumulative probability density function of the performance index. Recent mu-analysis tools implemented in the ONERA's SMAC toolbox are coupled with a dichotomic search algorithm in order to delimite the safe parametric domain while incrementing the probability of success of criteria. The proposed algorithm is applied to a didactic second order system to demonstrate the performances of the method.

Keywords: Continuous time system estimation, Software for system identification, Filtering and smoothing
Abstract: This paper presents a non-integer order (fractional) derivative model for modeling lithium-ion (Li-ion) battery dynamics in which direct continuous-time (CT) model identification methods are used to estimate the battery model parameters. In particular, the CT least squares-based state-variable filter (lssvf) identification method is extended to be used in fractional differential model identification of a battery system. The model performance and validation accuracy are evaluated on the basis of experimental data of a Li-ion polymer (LiPo) battery. It is shown how the proposed lssvf identification method can accurately capture the fractional order battery dynamics and exhibit better performance than integer order differential models.

Keywords: Fault detection and diagnosis, Filtering and smoothing, Bayesian methods
Abstract: This paper develops a conceptual framework for network security verification, and provides an understanding of the inherent difficulties that arise due to two phenomena: (a) incomplete information, e.g., due to observation noise, and (b) rare observations, e.g., those triggered by an attack or very low probability failures. A Hidden Markov Model (HMM) is adopted as an abstraction of the network dynamics and information structure. It is shown that due to incomplete state information, certain rare observations can completely overhaul strong prior beliefs about the states of the network. In particular, certain rare observations may lead to situations in which the entropy of the best estimate of the past (or current) states becomes unacceptably large. Furthermore, these phenomena can occur even when the noise in sensory information is arbitrarily small. Simulation results over random, random regular, and scale free networks highlight remarkable similarities in patterns of emergence of fragility across different network topologies. These results are relevant for security applications such as intrusion detection, failure recovery and digital forensics in complex networks and cyber-physical systems.

Keywords: Fault detection and diagnosis, Vibration and modal analysis, Mechanical and aerospace estimation
Abstract: The monitoring of mechanical systems aims at detecting damages at an early stage, in general by using output-only vibration measurements under ambient excitation. In this paper, a method is proposed for the detection and isolation of small changes in the physical parameters of a linear mechanical system. Based on a recent work where the multiplicative change detection problem is transformed to an additive one by means of perturbation analysis, changes in the eigenvalues and eigenvectors of the mechanical system are considered in the first step. In a second step, these changes are related to physical parameters of the mechanical system. Finally, another transformation further simplifies the detection and isolation problem into the framework of a linear regression subject to additive white Gaussian noises, leading to a numerically efficient solution of the considered problems. A numerical example of a simulated mechanical structure is reported for damage detection and localization.

Keywords: Mechanical and aerospace estimation, Bayesian methods, Particle filtering/Monte Carlo methods
Abstract: This paper proposes a composite Bayesian filtering approach for unmanned aerial vehicle trajectory estimation in cluttered environments. More specifically, a complete model for the measurement likelihood function of all measurements, including target-generated observation and false alarms, is derived based on the random finite set theory. To accommodate several different manoeuvre modes and system state constraints, a recursive multiple model Bayesian filtering algorithm and its corresponding Sequential Monte Carlo implementation are established. Compared with classical approaches, the proposed method addresses the problem of measurement uncertainty without any data associations. Numerical simulations for estimating an unmanned aerial vehicle trajectory generated by generalised proportional navigation guidance law clearly demonstrate the effectiveness of the proposed formulation.

Keywords: Mechanical and aerospace estimation, Estimation and filtering, Nonlinear system identification
Abstract: In the aerospace industry the (multiplicative) extended Kalman filter (EKF) is the most common method for sensor fusion for guidance and navigation. However, from a theoretical point of view, the EKF has been shown to possess local convergence properties only under restrictive assumptions. In a recent paper, we proved a slight variant of the EKF, namely the invariant extended Kalman filter (IEKF), when used as a *nonlinear observer*, possesses local convergence properties under the same assumptions as those of the linear case, for a class of systems defined on Lie groups. This is especially interesting as the IEKF also retains all the desirable features of the standard EKF, especially its relevant tuning in the presence of noises. In the present paper we provide three examples of engineering interest where the theory is shown to apply, yielding three EKF-like algorithms with guaranteed local convergence properties. Beyond those contributions, the present article is sufficiently accessible to help the practitioner understand through concrete examples the general IEKF theory, and to provide him with guidelines for the design of IEKFs.

Keywords: Vibration and modal analysis, Fault detection and diagnosis
Abstract: The majority of steel produced today is made by the technology known in the steel making industry as continuous casting. Deciding when to stop the flow of molten steel from the ladle is not trivial, since terminating the process too early affects yield negatively, while closing the outflow valve too late lets slag enter the casting process. There is a variety of automatic slag detection systems available now, but numerous casting operations still rely on the decision of a human operator. In this paper, we propose a cost-effective non-invasive slag detection system that is based on the vibration signal measured during the casting procedure. In this method, the vibration acceleration data is analyzed by a cumulative sum (CUSUM) control chart in real time, providing a violation signal that can be used to close the ladle outflow valve. The proposed algorithm is implemented in an embedded microcontroller unit and is verified through a simulation study and laboratory experiments. These trials suggest that the technique may perform similarly to the human operator, however, just as in the case of the human operator, the disadvantage is that it only identifies the change when a small amount of slag already enters the tundish. Its advantage lies in its simplicity, low-cost, portable and non-invasive nature; possibly aiding the decision of the operator or, it may be used to create a completely automated ladle outflow valve closing system.

Keywords: Nonlinear system identification
Abstract: Nonlinear system identification is a fast evolving field of research with contributions from different communities. It is not always straightforward to compare different models and identification approaches. Therefore it is of high importance to offer well chosen benchmarks to the nonlinear system identification community. The interaction generated by such a benchmark can push the state of the art in nonlinear system identification forward. This paper discusses some challenges that are present nowadays in the nonlinear system identification community. Three benchmarks are presented, a hysteretic benchmark, a Wiener-Hammerstein benchmark, and a cascaded tanks benchmark, each addressing one or more of these challenges.

Keywords: Nonlinear system identification, Frequency domain identification, Nonparametric methods
Abstract: Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. A typical example of such systems is the cascaded water-tanks benchmark. This benchmark combines soft and hard nonlinearities to be identified based on relatively short data records. In this paper, a methodology to identify an unstructured flexible nonlinear state space model (NLSS) for the cascaded water-tanks benchmark is proposed. The flexibility of the NLSS model structure is demonstrated by introducing two different initialisation schemes. Furthermore the strengths and short-comings of the model structure are discussed with respect to the cascaded water-benchmark identification problem.

Keywords: Nonlinear system identification, Frequency domain identification, Vibration and modal analysis
Abstract: Hysteresis is a nonlinear effect that shows up in a wide variety of engineering and scientific fields. The identification of hysteretic systems from input-output data is an important but challenging question, which has been studied by using both tailored parametric white-box identification methods as by using black-box identification methods. The white-box modeling approach is by far the most common in identifying hysteretic systems, and has the advantage of resulting into an interpretable model, but it requires to be adjusted to a specific hysteresis model. A black-box approach can be used more universally, but results in models containing many parameters that cannot easily be interpreted. In the current paper, we propose a two-step identification procedure that combines the best of the two approaches. We employ the Bouc-Wen hysteretic model to generate data that is used for identification. The system is identified using a black-box polynomial nonlinear state-space identification procedure. We reduce the number of parameters in this model by applying a polynomial decoupling method that results in a more parsimonious representation. We compare the full black-box model with the decoupled model and show that the proposed method results in a comparable performance, while significantly reducing the number of parameters.

Keywords: Nonlinear system identification, Grey box modelling, Continuous time system estimation
Abstract: The purpose of this study is to tackle the nonlinear system identification benchmarks proposed by (Schoukens and Noël, 2016). Two of the three benchmarks are considered, namely the cascaded tanks setup and the Bouc-Wen hysteretic system. Our approach is an output error method based on continuous time models. Due to the nonlinearities, the derivatives of the output with respect to the parameters are not defined everywhere. We compare the performance of two derivative-free optimisation solvers: the Nelder-Mead simplex and the NOMAD algorithm. Both are available in the OPTI Toolbox. The results suggest that the method is appropriate for those systems. However, it is not possible to discriminate between both optimisation solvers.

Keywords: Nonlinear system identification, Identification for control, Software for system identification
Abstract: In this paper an automated model generation framework is used to identify three nonlinear dynamic benchmark processes. The nonlinearity is approximated using tree-based Local Model Networks (LMN) with external dynamics, which are represented by three different approaches: NARX, NFIR and NOBF. The automated method assumes no prior knowledge about the process, and aims to be a ready-to-use tool for system identification. Results are given for the different approaches and benchmark processes. The importance of the choice of training data for a good generalizing model performance is discussed.

Keywords: Nonparametric methods
Abstract: This paper presents an efficient nonparametric time domain nonlinear system identification method applied to the measurement benchmark data of the cascaded water tanks. In this work a method to estimate efficiently finite Volterra kernels without the need of long records is presented. This work is a novel extension of the regularization methods that have been developed for impulse response estimates of linear time invariant systems. Due to the limited number of available data samples, the highest considered Volterra order is limited. In the paper the results for different scenarios varying from a simple Finite Impulse Response (FIR) model to a 3rd degree Volterra series are compared and studied. In each case, the transients are removed by a special regularization method based on the novel ideas of transient removal for Linear Time-Varying (LTV) systems. Using the proposed methodologies, the nonparametric Volterra models provide a very good data-fit, and their performance is comparable with the white-box (physical) models.

Keywords: Real-time control, Aerospace
Abstract: In this paper, we propose a strategy to solve endo atmospheric launch vehicle optimal control problems using indirect methods. More specically, we combine shooting methods with an adequate continuation algorithm, taking advantage of the knowledge of an analytical solution of a simpler problem. This procedure is resumed in two main steps. We first simplify the physical dynamics to obtain a new analytical guidance law which is used as initial guess for a shooting method. Then, a continuation procedure makes the problem converge to the complete dynamics leading to the optimal solution of the original system. Numerical results are presented.

Keywords: Optimal control theory, Hamiltonian trajectories in optimal control, Systems biology
Abstract: In this article, the model of swimming at low Reynolds number introduced by D. Takagi (2015) to analyze the displacement of an abundant variety of zooplankton is used as a testbed to analyze the motion of symmetric microswimmers in the framework of optimal control theory assuming that the motion occurs minimizing the energy dissipated by the fluid drag forces in relation with the concept of efficiency of a stroke. The maximum principle is used to compute periodic controls candidates as minimizing controls and is a decisive tool combined with appropriate numerical simulations using indirect optimal control schemes to determine the most efficient stroke compared with standard computations using Stokes theorem and curvature control. Also the concept of graded approximations in SR-geometry is used to evaluate strokes with small amplitudes providing a fixed displacement and minimizing the dissipated energy.

Keywords: Non-smooth and discontinuous optimal control problems, Optimal control theory, Control of partial differential equations
Abstract: Nonsmooth nonconvex optimization problems are considered in infinite dimensional sequence spaces l^p with p in (0, 1]. Our starting points are necessary optimality conditions in the form of a complementary system and a monotonically convergent algorithm for a regularized version of the original problem. We propose an algorithm for solving the necessary optimality condition based on a combination of the monotone scheme and an active-set strategy. Numerical results for different test cases are provided, e.g. for optimal control problems and microscopy image reconstruction.

Keywords: Optimal control theory, Hamiltonian trajectories in optimal control, Control in neuroscience
Abstract: The object of this paper is to study the uniqueness of solutions of inverse control problems in the case where the dynamics is given by a control-affine system without drift and the costs are length and energy functionals.

Keywords: Control of constrained systems, Optimal control theory, Non-smooth and discontinuous optimal control problems
Abstract: We consider the problem of minimizing the cost h(x(T)) at the endpoint of a trajectory x subject to the finite dimensional dynamics x' in - N_C(x) + f(x,u), x(0)=x_0, where N_C denotes the normal cone to a convex set C. Such differential inclusion is termed, after Moreau, sweeping process. We label it as a nonclassical control problem with state constraints, because the right hand side is discontinuous with respect to the state, and the constraint x(t) in C for all t is implicitly contained in the dynamics.

We prove necessary optimality conditions in the form of Pontryagin Maximum Principle by requiring, essentially, that C is independent of time. If the reference trajectory is in the interior of C, necessary conditions coincide with the usual ones. In the general case, the adjoint vector is a BV function and a signed vector measure appears in the adjoint equation.

Keywords: Optimal control theory, Control design for hybrid systems
Abstract: We study the optimal control problem where the cost functional to be minimized represents the so-called time of crisis, i.e. the time spent by a trajectory solution of a control system outside a given set K. Such a problematic finds applications in population dynamics, such as in prey-predator models, which require to find a control strategy that may leave and enter the crisis domain K a number of time that increases with the time interval. One important feature of the time crisis function is that it can be expressed using the characteristic function of K that is discontinuous preventing the use of the standard Maximum Principle. We provide an approximation scheme of the problem based on the Moreau-Yosida approximation of the indicator function of K and prove the the convergence of an optimal sequence for the approximated problem to an optimal solution of the original problem when the regularization parameter goes to zero. We illustrate this approach on a simple example and then on the Lotka-Vo

Keywords: Nonlinear predictive control, Asymptotic stabilization, Lyapunov methods
Abstract: In this paper, we first introduce upper and a lower bounds of the best asymptotic average performance for nonlinear control systems based on the concepts of dissipativity and control storage functions. This allows to extend the formulation and analysis of Economic Model Predictive Control to more general optimal operation regimes, such as periodic solutions. A performance and stability analysis is carried out within this generalized framework. Finally two examples are proposed and discussed to show the merits of the proposed approach.

Keywords: Nonlinear predictive control, Stochastic optimal control problems
Abstract: The optimization of process economics within the model predictive control (MPC) formulation has given rise to a new control paradigm known as economic MPC (EMPC). Several authors have studied the closed-loop properties of EMPC-controlled deterministic systems, however, little have uncertain systems been studied. In this paper we propose EMPC formulations for nonlinear Markovian switching systems which guarantee recursive feasibility, asymptotic performance bounds and constrained mean square stability.

Keywords: Control of constrained systems
Abstract: This paper introduces OPTIPLAN - a Matlab-based toolbox for formulating, solving, and simulating model predictive controllers (MPC) with embedded obstacle avoidance functionality. The toolbox offers a simple, yet powerful user interface that allows control researchers and practitioners to set up even complex control problems with just a few lines of code. OPTIPLAN fully automates tedious mathematical and technical details and let the user concentrate on the problem formulation. It features a rich set of tools to perform closed-loop simulations with MPC controllers and to visualize the results in an appealing way. From a theoretical point of view, OPTIPLAN tackles non-convex obstacle avoidance constraints in two ways: either by using binary variables or by resorting to suboptimal, but convex, time-varying constraints.

Keywords: Nonlinear predictive control, Power systems
Abstract: This paper presents an improved Bernstein global optimization algorithm based model predictive control (MPC) scheme for nonlinear systems. A new improvement in the Bernstein algorithm is the introduction of a box pruning operator, which during a branch-and-bound search, discards portions of the solution search-space that do not contain the global solution, thereby speeding up the algorithm. The applicability of this MPC scheme is demonstrated with a simulation study on a nonlinear single machine infinite bus power system. The simulation results show improvements in the system damping and settling time compared with the classical power system stabilizer and partial feedback linearization control schemes.

Keywords: Nonlinear predictive control, Real-time control, Control of renewable energy resources
Abstract: Efficient integration schemes with sensitivity propagation are crucial for deploying real-time Nonlinear Model Predictive Control on systems described by continuous time dy- namics. Implicit integration schemes are preferred when stiff modes are present in the model equations, or when the equations are implicit. We consider here a class of models, where the dynamics are linear, but coupled to a general static nonlinear feedback function. We propose a collocation-based implicit integration scheme where a lifting-condensing approach is used to exploit this specific structure to reduce the size of the linear algebra underlying the integrator. This technique yields a significant reduction in the computational complexity of performing the system integration and sensitivity analysis, when the static nonlinearity is of much smaller dimensions than the complete dynamics. The proposed method is illustrated on a complex wind turbine model, resulting in a significant gain of computational time in the linear algebra, and an overall gain of computational time of a factor 2.

Keywords: Nonlinear predictive control, Uncertainty descriptions, Control under communication constraints (nonliearity)
Abstract: This paper addresses congestion control for networks. The main focus is the modeling and compensation of nonlinear disturbance in the network, which is seldom considered in the literature. A new congestion control method is presented to compensate for the effects of nonlinear disturbance, uncertainty, time-varying delay, and input constraint. A state feedback congestion controller is designed via model predictive control approach. The stability of the closed-loop system is analyzed by using Lyapunov-Krasovskii functional. The effectiveness and feasibility of the proposed controller are verified by simulation results.

Keywords: Chemical engineering, Lyapunov methods
Abstract: Chemical reaction rate, also known as the reaction flux involved in chemical reactors, plays a central role as the source generating the abnormal dynamics characteristics. This paper proposes a structural approach for the stabilization of such systems through the control of the reaction flux by considering the Lyapunov stability theory within a standard thermodynamic framework. More precisely, the reaction flux is structurally considered as a nonlinear function of conjugated reaction force. The thermodynamic constraint of such a relationship is that the inherent non-negative definiteness property of the irreversible entropy production due to chemical reaction has to be fulfilled. Consequently, it allows to re-express a large class of reaction rates described by the mass-action-law and more interestingly, the operation of the reaction system at a desired set-point consists in controlling the reaction force on the basis of an affinity-related storage function. Numerical simulations for a non isothermal continuous stirred tank reactor (CSTR) involving one reversible reaction operated with multiple steady states illustrate the application of the theoretical developments.

Keywords: Stability of nonlinear systems, Passivity-based control
Abstract: The second law imposes a number of constraints on the set of feasible reaction rate coefficients that a chemical network can accept, known in the literature on CRNT (Chemical Reaction Network Theory) as Wegscheider's condition. % One main consequence is that any closed chemical reaction system operating at isothermal conditions, if compatible with thermodynamics, must be complex balanced, otherwise the Wegscheider's condition is violated.

In this contribution we make use of this observation to conclude passivity of open reaction systems with respect to a Gibbs energy function, what in turn allows us to point on mass and energy exchanges with the environment, as the main causes that make chemical reactors unstable. As it will be discussed, control of open reaction systems can take advantage of this fact to design input-output stabilizing feed-back controllers.

Keywords: Chemical engineering, Passivity-based control, Output regulation
Abstract: Abstract On this contribution a cascade control for a class of continuous chemical reactors is presented. Given that the internal entropy production of the reacting systems under study can be considered as a storage function, the inner loop is designed to shape the entropy production using the outflow rate as the control variable. The tracking reference for the entropy production of the inner loop is computed by an outer loop that regulates a typical process variable such as temperature, or pressure inside the reactor. In the resulting cascade control structure, the inner loop guarantees the stability of the reacting system, while the outer loop can be used to fulfill safety or operational conditions.

Keywords: Passivity-based control, Lagrangian and Hamiltonian systems, Stability of nonlinear systems
Abstract: In this work irreversible port-Hamiltonian systems are used to derive a passivity based controller which shapes the total energy of a non-isothermal reaction network and renders it asymptotically stable with respect to a desired dynamic equilibrium configuration. The closed- loop system is in IPHS form, hence it can be identified with a desired reaction network and the control parameters are related with thermodynamic variables, such as the reaction rates. A complex reaction network is used to illustrate the approach: the van der Vusse reaction mechanism.

Keywords: Lagrangian and Hamiltonian systems, Energy systems, Control of interconnected systems
Abstract: This contribution reports on an ongoing research project aimed in developing a unified theoretical framework for the description of interconnected thermo-mechanical systems with a particular emphasis on thermodynamic engines. We analyse from the geometrical viewpoint the structure of thermodynamic and mechanical interconnection and propose an approach to the unified description of thermo-mechanical systems. The theoretical results are illustrated by a physical example.

Keywords: Stability of nonlinear systems, Process control, Passivity-based control
Abstract: We consider the problem of feedback control design for thermodynamic systems. Following past contributions, we consider the expression of closed-loop control in the Thermodynamic Phase Space (TPS) using contact geometry. Some of these past contributions identified restricted classes of admissible controllers by restricting the dynamics to a Legendre submanifold in the TPS. In this paper, we consider the problem of controlling a system through damping feedback control. By allowing this particular class of feedback design technique, we characterize the impact of control on the thermodynamic structure of the closed-loop system in the TPS. An example is considered to illustrate the proposed construction.

Keywords: Coordination of multiple vehicle systems, Multi-agent systems, Distributed control and estimation
Abstract: In formation control, triangular formations consisting of three autonomous agents serve as a class of benchmarks that can be used to test and compare the performances of different controllers. We present an algorithm that combines the advantages of both position- and distance-based gradient descent control laws. For example, only two pairs of neighboring agents need to be controlled, agents can work in their own local frame of coordinates and the orientation of the formation with respect to a global frame of coordinates is not prescribed. We first present a novel technique based on adding artificial biases to neighboring agents' range sensors such that their eventual positions correspond to a collinear configuration. Right after, a small modification in the bias terms by introducing a prescribed rotation matrix will allow the control of the bearing of the neighboring agents.

Keywords: Multi-agent systems, Distributed control and estimation, Control and estimation with data loss
Abstract: In this work we introduce an algorithm for distributed average consensus which is able to deal with asynchronous and unreliable communication systems. It is inspired by two algorithms for average consensus already present in the literature, one which deals with asynchronous but reliable communication and the other which deals with unreliable but synchronous communication. We show that the proposed algorithm is exponentially convergent under mild assumptions regarding the nodes update frequency and the link failures. The theoretical results are complemented with numerical simulations.

Keywords: Multi-agent systems, Cooperative systems
Abstract: In this paper, the problems of (modal) consensus and formation control are tackled for a group of heterogeneous agents described by linear dynamics and communicating over a network with fixed topology. The classic approach to these problems prescribes that each agent is provided with its own complete internal model of the desired dynamics (which can be viewed as an exosystem). In this paper, the novel concept of Distributed Endogenous Internal Model is introduced and discussed. Such internal model is characterized by two features: i) it is actually distributed over the network, i.e. no single agent is provided with a complete internal model; ii) it is endogenous, namely it is generated by exploiting the dynamics already available to the overall group of agents, through the local cooperation between each agent and its neighbors. As a consequence, each agent is capable of generating the desired steady-state distributed static control input by only exchanging information with its neighbors, without the need for additional dynamics anywhere in the network.

Keywords: Multi-agent systems, Sensor networks, Hybrid and switched systems modeling
Abstract: In this paper, we present an algorithm for pose control of a team of mobile sensors for coverage and inspection applications. The region to cover is abstracted into a finite set of landmarks, and each sensor is responsible to cover some of the landmarks. The sensors progressively improve their coverage by adjusting their poses and by transferring the ownership of some landmarks to each other. Inter-sensor communication is pairwise and intermittent. The sensor team is formally modeled as a multi-agent hybrid system, and an invariance argument formally shows that the team reaches an equilibrium configuration, while a global coverage measure is improving monotonically. A numerical simulation corroborates the theoretical results.

Keywords: Multi-agent systems
Abstract: This paper is concerned with the problem of finite-time consensus under directed communication topologies for multi-agent systems with double-integrator dynamics. First, a relationship between the weights of in-edges and the ones of out-edges for each agent is established and is used in finite-time consensus analysis. Second, based on our previously proposed finite-time consensus controller, the consensus conditions in the framework of directed topologies are derived. Finally, the effectiveness of the finite-time consensus conditions is illustrated by numerical examples.

Keywords: Multi-agent systems, Distributed control and estimation, Cooperative systems
Abstract: This paper is concerned with the consensus problem of a multi-agent system where some nodes can act as leaders and they are allowed to inject input signals to improve some group dynamics performances. After a brief introduction of some controller structures, the analysis is focused on those control laws allowing the group to reach a consensus on a linear function of the initial conditions of the whole group. Afterwards, the group dynamics is analyzed in terms of characteristic polynomial of the controlled system. As a straight application to compare them, we consider the design of a set of controllers to achieve a finite time consensus. Finally, an example on the proposed strategy is shown and simulation results are reported to validate theoretical results developed in the paper.

Keywords: Linear systems
Abstract: Rudolf Emil Kalman passed away peacefully on 2 July 2016, at his home in Gainesville, Florida. He was 86 years old. His passing marks the end of an era. This paper is to remember and celebrate the life, the works, and the impact of a giant whose influence extends over several fields and who is considered the founder of the modern theory of systems and control.

Keywords: Linear systems, Time-invariant systems, Digital implementation
Abstract: This note gives a brief historical account on Rudolf Kalman's influence on modern system theory, particularly, state space theory, realization, and sampled-data systems.

Kalman made fundamental contributions to modern system and control theory, and laid the foundation of the field. Among such contributions, we review some aspects of state space theory in the context of 1960, and his ideas and philosophy for realization theory, and also look at the pioneering work on sampled-data control. We will view and examine their impacts on the field today.

Keywords: Linear systems, Model validation
Abstract: Kalman's original work was quickly adopted and explored throughout the engineering disciplines after the publishing of his seminal paper in 1960 (Kalman 1960). However, it would be several decades before the utility of his framework was understood and adopted in the econometrics discipline, which had traditionally based its analysis on the Box-Jenkins ARIMA time series models. This paper surveys how Kalman's work has been applied in developing econometric models and how econometricians have extended and built upon Kalman's framework to develop new techniques to model complex economic and financial systems over the past 20-25 years.

Keywords: Structural properties, Optimal control theory
Abstract: Professor R. E. Kalman passed away on July 2, 2016. His remarkable impact on the field of system and control theory and wider domains of science and engineering is now enshrined in text books, archival scientific and engineering literature, and numerous products and achievements of modern technology. In this paper, I will share some thoughts on his way of thinking about research, science, and engineering.

Keywords: Positive systems, Linear systems, Structural properties
Abstract: The present paper is a tribute to Professor Rudolf Emil Kalman, father of Mathematical System Theory and a towering figure in the field of Control and Dynamical Systems. Amongst his seminal contributions was a series of results and insights into the role of positivity in System Theory and in Control Engineering. The paper contains a collection of reminiscences by the author together with brief technical references that touch upon the unparalleled influence of Professor Kalman on this topic.

Keywords: Linear systems, Optimal control theory
Abstract: The aim of the paper is to highlight one of the seminal discoveries of Kalman – the central role the Riccati equation plays in optimal control and optimal estimation – and show how this result inspired researchers (including myself) to elaborate the subject and obtain remarkable results.

Keywords: control and estimation of wave equations and systems of elasticity, Control of constrained systems, stability of distributed parameter systems
Abstract: In this paper, boundary control schemes are designed for a flexible aerial refueling hose subject to input deadzone, output constraint and external disturbances, based on partial differential equations (PDEs). Firstly, a basic boundary control scheme, based on the backstepping method, is proposed to regulate the hose’s vibration. A radial basis function (RBF) neural network is used to handle the effect of the input deadzone. Then we propose a new controller based on a barrier Lyapunov function to prevent constraint violation. It is shown that the states of the system are proven to converge to a small neighborhood of zero, and the output constraint is not violated in the presence of the input deadzone and external distrubances. Finally, simulation results are demonstrated for control performance verification.

Keywords: backstepping control of distributed parameter systems, output regulation for distributed parameter systems, delay compensation for linear and nonlinear systems
Abstract: In this paper, we are concerned with the output regulation of an unstable reaction-diffusion PDE with regulator delay and unmatched disturbances, which are generated by an exosystem. A backstepping regulator design procedure is presented by mapping the reaction-diffusion PDE cascaded with a transport equation into an error system, which is shown to be exponentially stable with a prescribed rate in a suitable Hilbert space. The output regulation is then verified by solving cascaded regulator equations. The solvability condition of the cascaded regulator equations is characterized by a transfer function and eigenvalues of the exosystem. Finally, the numerical simulations are provided to illustrate the effect of the regulator.

Keywords: stability of distributed parameter systems, Time-varying systems
Abstract: In the framework of the consensus-based time synchronization (CBTS) algorithm structure, this paper gives a constant gain approach to the design of time synchronization algorithms for wireless sensor networks (WSNs) with random bounded communication delays. Under the assumption that the asynchronous updating topology is uniformly rooted (UR), it is proved that this design can ensure the asymptotic convergence of drift estimations and the boundedness of the global synchronization errors even if we do not compensate the offsets. A numerical experiment is presented to illustrate the effectiveness of this algorithm.

Keywords: control of hyperbolic systems and conservation laws
Abstract: In this paper, we present an output feedback controller for systems consisting of n 2x2 semilinear hyperbolic systems in series interconnection where actuation and sensing are restricted to one boundary. The output-feedback control law consists of a state-feedback controller combined with an observer. The control and estimation laws are based on the dynamics on the characteristic lines of the hyperbolic system, and achieve stabilization of the origin or tracking at one location, and full state estimation, respectively, globally and in minimum time. We demonstrate the controller performance in a numerical example, and apply the controller to a relevant disturbance rejection problem in oil well drilling.

Keywords: control of heat and mass transfer systems, Discontinuous control, stability of distributed parameter systems
Abstract: The paper deals with boundary finite-time control for heat system. A linear switching control with state dependent switchings is designed based on backstepping procedure. It steers any solution of the heat system to zero in a finite time. The theoretical results are supported by numerical simulations.

Keywords: port Hamiltonian distributed parameter systems, Infinite-dimensional systems, semigroup and operator theory
Abstract: It is shown that the class of infinite-dimensional nice port-Hamiltonian systems including a large range of distributed parameter systems with boundary control is a subclass of Riesz-spectral systems. This result is illustrated by an example of a vibrating string.

Keywords: Structural properties, Polynomial methods
Abstract: Consider a nonlinear input-affine control system x'(t)=f(x(t))+g(x(t))u(t), y(t)=h(x(t)), where f,g,h are polynomial functions. Let S be a set given by algebraic equations and inequations (in the sense of non-equality). Such sets appear, for instance, in the theory of the Thomas decomposition, which is used to write a variety as a disjoint union of simpler subsets. The set S is called controlled invariant if there exists a polynomial state feedback law u(t)=alpha(x(t)) such that S is an invariant set of the closed loop system x'=(f+g*alpha)(x). If it is possible to achieve this goal with a polynomial output feedback law u(t)=beta(y(t)), then S is called controlled and conditioned invariant. These properties are discussed and algebraically characterized, and algorithms are provided for checking them with symbolic computation methods.

Keywords: Structural properties, Application of nonlinear analysis and design
Abstract: The paper describes a symbolic computation software package NLControl, dedicated to the study of various modelling, analysis, synthesis problems for nonlinear control systems. Most of the developed functions are based on algebraic approach of differential forms, and on the theory of non-commutative polynomial rings. For a few control problems an alternative solution is implemented in terms of functions algebra. The package addresses both continuous- and discrete-time systems as well as systems, defined on homogeneous time scales. Comparison of NLControl and Mathematica built-in nonlinear control functions is given.

Keywords: Observers for linear systems, Time-invariant systems, Linear systems
Abstract: In this paper we extend the state space geometric notions of conditioned invariance and detectability subspaces to the behavioral framework. This is achieved based on the existing notions of behavioral tracking observer and of behavioral asymptotic observer introduced by Valcher, Willems, Trentelman and Trumph, combined with a notion of behavioral invariance introduced here. Moreover, we provide characterizations for the newly defined behavioral properties.

Keywords: Robust control (linear case), Linear systems
Abstract: The internal model principle is a fundamental result stating a necessary and sufficient condition for a stabilizing controller to be robustly regulating. Its classical formulation is given in terms of coprime factorizations and the largest invariant factor of the signal generator which sets unnecessary restrictions for the theory and its applicability. In this article, the internal model principle is formulated using a general factorization approach and the generators of the fractional ideals generated by the elements of the signal generator. The proposed results are related to the classical ones.

Keywords: Robust control (linear case), Polynomial methods, Linear systems
Abstract: The purpose of this paper is to explicitly characterize H∞ controllers for single-input single-output (SISO) systems of order 1, 2 and 3 in terms of their coefficients considered as unknown parameters. In the SISO case, computing H∞ controllers requires to find the real positive definite solution of an algebraic Riccati equation. Due to the system parameters, no purely numerical method can be used to find such a solution, and thus parametric H∞ controllers. Using elimination techniques of zero-dimensional polynomial systems, we first give a parametrization of all the solutions of the algebraic Riccati equation associated with the H∞ control problem. Since the problem reduces to solving an univariate polynomial of degree less than or equal to 4, closed-form solutions are then obtained for the solutions of the algebraic Riccati equation by means of radicals. Using the concept of discriminant variety, we show that the maximal real root of this polynomial is always defined by the same closed-form expression, which yields the positive definite solution of the algebraic Riccati equation. Finally, we use the above results to explicitly compute the H∞ criteria and H∞ controllers in terms of the system parameters and study them with respect to parameter variations.

Keywords: Robust control (linear case), Polynomial methods, Linear systems
Abstract: The purpose of this paper is to explicitly characterize H∞ controllers for 4th order single-input single-output (SISO) systems in terms of their coefficients considered as unknown parameters. In the SISO case, computing H∞ controllers requires to find the real positive definite solution of an algebraic Riccati equation (ARE). Due to the system parameters, no purely numerical method can be used to find such a solution, and thus parametric H∞ controllers. Using elimination techniques for zero-dimensional polynomial systems, we first give a rational parametrization of all the solutions of the ARE. Then, as the problem reduces to solving polynomials of degree 4, closed-form solutions are obtained for all the solutions of this ARE by using expressions by radicals. Using the concept of discriminant variety, we then show that the maximal real root of one of these polynomials is encoded by two different closed-form expressions depending on the values of the system parameters, which yields to different positive definite solution of the ARE. The above results are then used to explicitly compute the H∞ criterion and H∞ controllers in terms of the system parameters. Finally, we study in detail a particular system: the two-mass-spring system with damping. Due to the low number of parameters, we can plot the variations of the H∞ criterion in function of the parameters, compute approximations of it at a working point, and derive the expression of a weight function of the parameters to set this H∞ criterion to a desired value.

Keywords: Switching control
Abstract: In this paper, a (switching) sliding mode controller, applied to a mechanical system with additive white process noise, is investigated. The practical relevance of this study is a statistical characterization of system performance in terms of the stationary variance of the control error. The system is modeled with a two-dimensional stochastic differential equation, whose coordinate functions to an extend are analyzed separately. In order to determine the stationary variance of one of the coordinate functions, the auto-correlation function for the other coordinate function is approximated with a Fourier series. Finally analytical results are compared to results from Euler-Maruyama simulation over a wide range of model parameter settings.

Keywords: Nonlinear adaptive control
Abstract: As shown in this paper, disturbance observer (DOB) based sliding mode control for fuzzy system is proposed. Compared with conventional sliding mode control, this method incorporated the information of disturbance estimations into the design of sliding mode controllers and sliding surface. The new approach can manage the problem of mismatched disturbance for fuzzy system effectively and those problems can’t be solved by the traditional sliding mode control. There are a disturbance observer, a sliding surface and a sliding mode controller with disturbance observer proposed in this paper. The adaptive sliding mode controller is also proposed to deal with the unknown upper bound of parameters. In the last part, the effectiveness of the new approach has been proved by the ball-beam system.

Keywords: Nonlinear adaptive control, Adaptive system and control
Abstract: Under a suitable condition on the zero-dynamics with time-delay, we prove that global state regulation can be achieved by a partial state adaptive controller for certain time-delay cascade systems with unknown parameters. A delay-independent, partial state adaptive control scheme is developed based on a dynamic gain-based design, the idea of changing supply rate and the feedback domination approach. With the help of appropriate Lyapunov-Krasovskii functionals, it is shown that the resulting closed-loop system is globally bounded and all the states of the time-delay cascade system converge to the origin.

Keywords: Linear parametrically varying (LPV) methodologies, Gain scheduling
Abstract: This paper deals with the total observability problem of discrete-time linear time-varying systems. In particular, a review and suitable analysis of the state-of-the-art of this emerging area are provided. Subsequently, the total observability problem of discrete-time linear time-varying systems is transformed into the one of checking the rank of a convex sum of matrices. As a result, a new total observability test is proposed, along with a suitable computational strategy. The final part of this paper shows examples regarding observability analysis that clearly exhibit the benefits of using the proposed approach.

Keywords: Adaptive control of networks, Switching control, Hybrid and switched systems modeling
Abstract: This paper studies the problem of averaging-based extremum seeking in dynamical multi-agent systems with time-varying communication graphs. We consider a distributed consensus-optimization problem where the plants and the controllers of the agents share information via time-varying graphs, and where the cost function to be minimized corresponds to the summation of the individual response maps generated by the agents. Although the problem of averaging-based extremum seeking control in multi-agent dynamical systems with time-invariant graphs has been extensively studied, the case where the graph is time-varying remains unexplored. In this paper we address this problem by making use of recent results for generalized set-valued hybrid extremum seeking controllers, and the framework of switched dierential inclusions and common Lyapunov functions. For the particular consensus-optimization problem considered in this paper, a semi-global practical stability result is established. A numerical example in the context of dynamic electricity markets illustrates the results

Keywords: Switching control, Adaptive system and control, Iterative modelling and control design
Abstract: This paper addresses a modeling and nonlinear control of photovoltaic systems which includes photovoltaic panels, a half bridge inverter with capacitive divider and an LCL filter connected to single-phase grid. We seek the achievement of three control objectives: i) ensure that the PV panels generate its maximum power ii) regulating the DC link voltage to track its reference provided by MPPT block, iii) achieve a satisfactory power factor correction (PFC) with respect to the power supply net. To meet these objectives a multi-loop controller is designed based on nonlinear approach applied to the 5th order nonlinear model. The simulation results have been performed through MATLAB/SimPowerSystems environment and show that the designed controller meets its objectives.

Keywords: Motion Control Systems, Robots manipulators, Design methodologies
Abstract: Optimization of anti-windup is achieved for non-linear systems with actuator saturation based on passivity. The systems are represented as Euler-Lagrange systems, and the proportional and integral (PI) controller, in which the gravity term is cancelled by a non-linear compensation technique, is used. The goal of optimization is to minimize the saturated input, i.e., the difference between the control input and the input limit, using the estimation function with a semi-positive convex function of the saturated input. The condition, including the time derivative of the gravity term, is necessary for the stability and optimization. The proposed controller and the optimality are verified through numerical simulations using a two-link robot arm.

Keywords: Motion Control Systems, Modeling, Guidance navigation and control
Abstract: Precise movement of a rigid body with jerk as the control signal, and with friction compensation is considered. The optimal solution for the minimization of the electrical energy consumption is obtained. The optimal solution was tested numerically.

Keywords: Motion Control Systems, Mechatronic systems
Abstract: This contribution presents a nonlinear model predictive torque control (MPTC) scheme for permanent magnet synchronous machines (PMSMs) to provide a desired torque in an energy efficient way over a wide operational range. The MPTC relies on the nonlinear dq-model %of the machine and uses a numerically efficient gradient method in combination with the augmented Lagrangian method to account for voltage and currents constraints. Simulation results show the performance and robustness of the control scheme as well as the computational efficiency and real-time feasibility.

Keywords: Motion Control Systems, Robotics technology
Abstract: This paper proposes a rolling motion scheme for Acrobot composed of two links with curved contours. A feature of the present rolling control is to introduce back bending motion with hope for no strong impact on the ground. First, the Acrobot model is classified into eight models according to the arcs in contact with the ground since the model differs with contact points on the ground. Rolling control strategy is next proposed by considering no impact conditions, kinetic energy and potential energy. Then, an output function, to be zero, is designed and output zeroing control is applied to achieve the rolling motion. Here, the output function is built by the two link angles and designed to follow a desired path given by a bezier curve. The curve is optimized to minimize the input energy while guaranteeing the achievement of rolling motion. Finally, simulation and an experiment are conducted for verification.

Keywords: Motion Control Systems, Identification and control methods, Design methodologies
Abstract: We present an approach to reduce the energy consumption of electromechanical drives by applying energy optimal trajectories under the presence of friction. The trajectories are calculated using Pontryagin's maximum principle and adapted online in order to minimize the energy consumption. We finally validate our results in simulations and thereby demonstrate an energy savings potential of 5% compared to utilizing energy optimal trajectories without the incorporation of friction.

Keywords: Motion Control Systems, Mobile robots, Mechatronic systems
Abstract: This study proposes a movement control system for a two-wheel inverted pendulum (IP) type robot, which is a personal robot that interacts with people. Safety and stability are important parameters in designing a robot for personal use. To achieve this, model predictive control (MPC) may be a suitable control technique. The controller presented in this study ensures safety in relation to velocity and stability by imposing time-invariant constraints on the controller input and body tilt angle. Finally, we present good validation results from a movement-control simulation of an IP robot.

Keywords: Smart Sensors and Actuators, Identification and control methods, Mechatronic systems
Abstract: Observer-based self sensing for digital (on-off) single-coil solenoid valves is investigated. Self sensing refers to the case where merely the driving signals used to energize the actuator (voltage and coil current) are available to obtain estimates of both the position and velocity. A novel observer approach for estimating the position and velocity from the driving signals is presented, where the dynamics of the mechanical subsystem can be neglected in the model. Both the effect of eddy currents and saturation effects are taken into account in the observer model. Practical experimental results are shown and the new method is compared with a full-order sliding mode observer.

Keywords: Virtual instruments, Reliable measurement and actuation, Sensor signal consolidation
Abstract: Machine Vision applied in the area of Instrumentation and Measurement is known as Vision Based Measurement. Its application in industry is steadily increasing principally due to he recent developments in computer hardware and computer vision algorithms. In this article we discuss the possibilities of combining industrial local sensors with a camera and a computer, thus obtaining a new type of transmitter based in Machine Vision. Thus, the possibility of transforming visual information to digital data, which can be readily available in an industrial network, opens a variety of possible new applications. As a case study, we propose and implement a Vision Based Measurement system capable of measuring and transmitting the value indicated by a pointer-scale indicator. To verify the applicability of the proposed transmitter the following results are presented: (i) the associated standard uncertainty is evaluated; (ii) the influence of the image size in the transmitter's uncertainty is investigated; (iii) the proposed transmitter is compared with a regular electronic transmitter and, (iv) its processing time is evaluated. The results obtained in laboratory are promising and indicate that the use of the proposed Machine Vision system in industry is a real possibility.

Keywords: Smart Sensors and Actuators, Perception and sensing, Robots manipulators
Abstract: The new applications for robots include tasks that were previously performed by specialized machines such as polish, milling, placement of fibers, among others. The implementation using robots in these new applications has the imprecisions problems due to the flexibilities within the robot arm or the tooling when external forces are applied. The industrial robot controller does not take into account the flexibilities and the external forces. This paper is an introduction of the work done to improve the precision in the robotic fiber placement, taking into in account the deformation in the compaction roller.

Keywords: Networks of sensors and actuators, Networked robotic system modeling and control
Abstract: This paper deals with the problem of retaining the connectivity in a Mobile Ad-hoc communication mesh Network (MANET). A multi-agent systems perspective is taken, where primary mobile agents (PAs) can only communicate when the relative distance is less than a “visibility range”. PAs form a network that can become disconnected depending on how they move to achieve their global task (which does not include the connectivity maintenance). To retain connectivity, a number of Relay Agents (RAs), whose motion is governed by a command center (CC), are sent to the field to act as “communication bridges” enforcing the global connectivity of the network containing both the PAs and the RAs. Graph-oriented concepts and analysis tools, particularly the minimal spanning tree (MST) notion, are adopted in the present work to analyze the connectivity properties of the network and to establish in real time how many additional RAs are required and how they should move in order to prevent the connectivity loss. Artificial potential fields and finite-time control techniques are utilized to drive the relay agents to their waypoints while avoiding the collisions. Numerical examples confirm the efficacy of the proposed multi-layer control strategy.

Keywords: Microsystems: nano- and micro-technologies, Smart Structures, Modeling
Abstract: This paper presents the design, analysis, and simulation study of a novel microelectromechanical system (MEMS) microgipper with two decoupled electrostatic actuators and two capacitive force sensors dedicated to the testing in compressive and shear directions of soft samples. The gripper mechanism is designed based on the two-prismatic-prismatic (2-PP) parallel structure and folded leaf flexure guiding mechanism. Owing to the design of decoupled actuation structure, the translations of the two gripping arms are parallel, which ensures the reaction force between two tips and grasped object in sole gripping direction. At the same time, this structure also offers a good decoupling property. The analytical model is verified by carrying out finite element analysis (FEA) simulation study, which confirms the feasibility and effectiveness of the proposed design. The fabrication procedure by SOIMUMPs process is also given, and the microgripper will be fabricated for future experimental study.

Keywords: Sensors and actuators, Health monitoring and diagnosis, Decision making and autonomy, sensor data fusion
Abstract: Commercial aircrafts use black box comprising a Flight Data Recorder (FDR) required for crash investigation purposes. While FDR can be easily recovered in crash events on land, the same does not apply to crash events in great deep ocean water. This paper presents a new solution towards solving FDR data recovery using a paradigm called "communicating materials". The solution is developed through uniformly integrating hundreds of tiny sensor nodes in the aircraft structure. The nodes could then construct a Wireless Sensor Network (WSN) inside the aircraft. Thus, the latest FDR data could be stored in the nodes using data storage protocol for WSN. The proposed storage protocol uses the probabilistic-based flooding scheme to forward data to all nodes inside the aircraft structure within the lowest delay. To improve reliability and collision-avoidance of flooding, different complementing corrective measures are used based on neighborhood information. The protocol is evaluated using Castalia/OMNeT++ simulator.

Keywords: Fuzzy and neural systems relevant to control and identification
Abstract: The present paper studies sitting control for persons with complete thoracic spine cord injury by means of automatic control. The methodology begins by modeling sitting control, after a discretization of the nonlinear descriptor model is computed, then a Takagi-Sugeno model is obtained for further manipulations. The approach uses unknown input observers to estimate states and internal variable of the system. The design conditions are expressed as linear matrix inequalities, whose solvability depends on convex optimization techniques. Simulations are conducted to show the effectiveness of the proposed observer.

Keywords: Fuzzy and neural systems relevant to control and identification, Adaptive neural and fuzzy control
Abstract: Takagi-Sugeno (T-S) type of polytopic models have been used prominently in the literature to analyze nonlinear systems. With the sector nonlinearity approach, an exact representation of a nonlinear system within a sector could be obtained in a T-S form. Hence, a number of observer design strategies have been proposed for nonlinear systems using the T-S framework. In this work, a design strategy for adaptive observers is presented for a type of T-S systems with unknown parameters. The proposed approach improves upon the existing literature in two folds: reduce the computational burden and provide an algorithmic procedure that would seamlessly connect the state estimation and parameter estimation parts of the observer design. Lyapunov approach is used for the stability analysis and the design procedure. The results are illustrated on a simulation example.

Keywords: Robust neural and fuzzy control, Fuzzy and neural systems relevant to control and identification
Abstract: This note shows that conservativeness of former results on stability analysis of uncertain systems based on the Kharitonov's Theorem can be significantly reduced by means of convex structures which lead to linear matrix inequalities. The relationship between an interval-coefficient family of polynomials and interval matrices is exploited in order to obtain novel LMI tests whose advantages are twofold: feasibility domains (both in ordinary as well as D-stability) are increased while LMIs are efficiently solved via convex optimization techniques. Illustrative examples are provided.

Keywords: Embedded computer control systems and applications, Fuzzy and neural systems relevant to control and identification
Abstract: For controlling the multi-robot formation system, a general projection neural network based nonlinear model predictive control (NMPC) strategy is proposed in this paper. The multi-robot formation system consists of the trajectory tracking of leader robot and the leader-follower formation controlling. Their kinematic error systems can be reformulated as a convex nonlinear minimization problem by the NMPC method and can be transformed into a constrained quadratic programming (QP) optimization problem. To solve this QP problem online efficiently, a general projection neural network (GPNN) is applied to obtain the optimal solution, which includes the optimal inputs of the formation system. Compare to other existing leader-follower approaches, the proposed nonlinear MPC method can take the input and state constraints of system into consideration. In the end of this work, simulations of the trajectory tracking and multi-robot formation are performed to verify the effectiveness of the developed strategy.

Keywords: Fuzzy and neural systems relevant to control and identification, Adaptive neural and fuzzy control
Abstract: In this paper, a novel adaptive Takagi-Sugeno (TS) fuzzy observer-based controller is proposed. The closed-loop stability and the boundedness of all the signals are proven by Lyapunov stability analysis. The proposed controller is applied to a flexible-transmission experimental setup. The performance for constant payload in the presence of noisy measurements is compared to a controller based on a classical extended Luenberger observer. Simulation and real-time results show that the proposed observer-based feedback controller provides accurate position tracking under constant and varying payloads.

Keywords: Fuzzy and neural systems relevant to control and identification, Robust neural and fuzzy control
Abstract: In this paper, the problem of local observer design for discrete-time Takagi-Sugeno fuzzy systems is addressed. The observers being designed are local in the sense that relaxed asymptotic convergence conditions are sought locally, rather than globally, while also determining the region where the conditions are valid. The design conditions are derived using both quadratic and non-quadratic Lyapunov functions. The obtained results are expressed in terms of linear matrix inequalities and they are illustrated on a numerical example.

Keywords: Bio-signals analysis and interpretation, Intensive and chronic care or treatment, Cellular, metabolic, cardiovascular, pulmonary, neuro-systems
Abstract: Pulse wave velocity (PWV) measurements are commonly used to evaluate a patient’s arterial stiffness, an indicator of cardiovascular dysfunction. PWV is usually calculated by measuring the pulse transit time (PTT) over a known distance through the arteries. In an experimental study on animals, it is straight forward to measure the PTT using two pressure catheters a known distance apart in the central arteries. However, in a clinical setting it is uncommon for such a direct invasive method to be used. This study aims to identify whether a surrogate measure of PTT could be found without the need for an external device and without being additionally invasive. The aim is to use the time between the R-wave of an electrocardiogram (ECG), and the pulse wave passing one pressure catheter (rPTT), both of which are common in critical care. The analysis was performed using data from four porcine experiments (Pietrain Pigs, 20-29kg) in which ECG, aortic arch pressure and abdominal aortic pressure were measured simultaneously over a range of induced hemodynamic conditions including recruitment manoeuvres (RM), fluid admission and dobutamine admission. From the measured data, the correlation of rPTT and PTT was calculated for each pig and condition. The overall results showed varied correlations across the pigs (r2 = 0.07 to 0.75). The variability is suspected to be due to two main causes, the first being pig specific response to the interventions. The second cause leading to poor correlation is suspected to be the pre-ejection period (PEP), the time following the ECG R-wave but before ejection of blood from the ventricle. The analysis showed that rPTT was an unreliable measure of PTT and a poor surrogate.

Keywords: Kinetic modeling and control of biological systems, Biomedical system modeling, simulation and visualization, Quantification of physiological parameters for diagnosis and treatment assessment
Abstract: Dominant Optic Atrophy is a neuro-ophthalmic disease characterized by the degeneration of the optic nerves. As other neurodegenerative diseases, this pathology was associated with dysfunctional mitochondrial respiratory complexes and an increased production of reactive oxygen species. Our objective is to create a mathematical model of the molecular mechanisms involved in the pathogenesis of Dominant Optic Atrophy in order to predict their evolution and give appropriated treatment based on in-silico analysis with physiological parameters from a specific patient. The first part of the work presented here concerns processes and feedback mechanisms of the whole dysfunctional mitochondrial system. The behaviour of the system is explained with a causal loop diagram. In the second part, we present a detailed stochastic model of catalytic activity and reactive oxygen species production of respiratory complex I. We developed the model using a Petri net formalism and a continuous-time Markov chains theory. The simulations were realized on Matlab and compared to kinetic data from the literature. Our model is able to reproduce the dynamics of the complex I system and to simulate observed behaviours of this system regarding ROS production.

Keywords: Identification and validation, Pharmacokinetics and drug delivery
Abstract: In this paper we analyse the use of dierent models to describe the effects of propofol in the induction of hypnosis on a patient during surgery. In particular, we consider the standard three-compartmental pharmacokinetics/pharmacodynamics Wiener model and a suitable reduction of it. The estimation of the parameters is based on real bispectral index scale surgical data and it is performed by using genetic algorithms. The results obtained for different patients show that the standard model fits the data better than the reduced model, but it is overparameterized and its structure is much more complex to be used for the control design. Conversely, the simplication of the identication procedure obtained with the reduced model, which has only three parameters, is paid by only a slight decrement of the performance.

Keywords: Identification and validation, Bio-signals analysis and interpretation, Clinical validation
Abstract: This study investigates blood glucose (BG) measurement interpolation techniques to represent intermediate BG dynamics, and the effect resampling of retrospective BG data has on key glycemic control (GC) performance results. Many GC protocols in the ICU have varying BG measurement intervals with gaps ranging from 0.5 to 4 hrs. Sparse data poses problems in model fitting techniques and GC performance comparisons, and thus interpolation is required to assume a continuous solution. Retrospective data from SPRINT in the Christchurch Hospital Intensive Care Unit (ICU) (2005-2007) was used to analyze various interpolation techniques. Piece-wise linear, spline and cubic interpolation functions, which force lines through data, as well as 1st and 2nd Order B-spline basis functions, used to identify the data, are investigated. Dense data was thinned to increase sparsity and obtain measurements (Hidden measurements) for comparison after interpolation. All of the piece-wise functions performed considerably better than the fitted interpolation functions. Linear piece-wise interpolation performed the best having a mean RMSE 0.39 mmol/L, within 2 standard deviations of the BG sensor error. The effect of minutely vs hourly sampling of the interpolated trace on key GC performance statistics was investigated using the retrospective data received from STAR GC in the Christchurch Hospital Intensive Care Unit (ICU), New Zealand (2011-2015). Minutely sampled BG resulted in significantly different key GC performance when compared to raw sparse BG measurements. Linear piece-wise interpolation provides the best estimate of intermediate BG dynamics and all analyses comparing GC protocol performance should use minutely linearly interpolated BG data.

Keywords: Decision support and control, Healthcare management, disease control, critical care, Biomedical system modeling, simulation and visualization
Abstract: Elevated blood glucose (BG) concentrations (Hyperglycaemia) is a common complication in the adult intensive care unit (ICU), and is associated with increased morbidity and mortality. However, it has been shown that effective glycaemic control (GC) can reduce morbidity and mortality. STAR is a model- based GC protocol that uniquely maintains normal BG by changing both insulin and nutrition interventions, and has been proven to be effective in controlling BG levels in the ICU. However, most GC protocols in the ICU only change insulin interventions, making the variable feed aspect of STAR less clinically desirable. This paper investigates two simpler feeding protocols as an alternative. Fixed feed (100% calorific goal) and stepped feed (60%, 80% and 100% calorific goal for the first 3 days of GC, and then 100% thereafter) protocols, in conjunction with the STAR protocol, are simulated with clinically validated virtual trials on a 221 virtual patient cohort. The GC safety, performance and workload is compared for each of the different feeding protocols. The variable and stepped feeding protocol achieved very similar GC performance and safety, with a per-patient median time in the targeted 4.4-8.0 mmol/L BG range of 89.0% vs. 88.3% respectively and the number of patients BG < 4.0 mmol/L being 77 vs. 78 respectively. In contrast, the fixed feeding protocol resulted in significantly poorer GC performance with 85.6% time in the targeted 4.4-8.0 mmol/L band (P<0.025). Both the fixed and stepped feeding protocols significantly reduced the number of feed changes required per day (6.4 variable vs. 0 fixed and 0.5 stepped, P<0.025). However, as a consequence a small increase in the number of BG measurements per day was seen (11.4 variable vs. 13.4 fixed vs. 12 stepped, P<0.025). Overall the stepped feeding protocol provides a simple alternative to the current variable feeding protocol, with similar GC safety and performance.

Keywords: Identification and validation, Artificial pancreas or organs, Biomedical system modeling, simulation and visualization
Abstract: Designing a fully automated artificial pancreas (AP) system is challenging. Changes in the glucose-insulin dynamics in the human body over time, and the inter-subject and day-to-day variability of people with type 1 diabetes (T1D) are two important factors that would highly undermine the performance of an AP that is based on time-invariant and non-individualized models. People with T1D show different responses to carbohydrate intake, insulin, physical activity and stress with day-to-day variability present between or within specific patients. Thus, the control law in an AP system requires a reliable time-varying individualized model to perform efficiently. In this work, a novel recursive identification approach called a Predictor-Based Subspace Identification (PBSID) method is used for identifying a linear time-varying glucose-insulin model for each individual. Model identification and validation are based on clinical data from closed-loop experiments. The models are evaluated by means of various performances indices: Variance Accounted For (VAF), Root mean square error (RMSE), Normalized root mean square error (NRMSE) and Normalized mean square error (NMSE). The proposed method provides a stable time-varying state space model over time. It can also be individualized for each patient by defining the order of the system correctly. The approach proposed in this work has shown a strong potential to identify a consistent glucose–insulin model in real time for use in an AP system.

Keywords: Disturbance rejection (linear case), Control design for hybrid systems, Linear systems
Abstract: In this paper the problem of disturbance decoupling is tackled for linear hybrid systems in the presence of state-driven jumps, induced by multi-affine sets. Firstly, it is shown that the definition of the control objective needs to be adapted to the hybrid setting, by distinguishing between weak and strong disturbance decoupling. In the former, the desired requirement consists in ensuring that the output of the plant is independent of the disturbance on each flowing sub-interval followed by a single jump, while the latter requires that both the output trajectory and the corresponding, possibly perturbed, time domain are not affected by the disturbance. Then, easily verifiable geometric necessary and sufficient conditions to achieve weak or strong decoupling are presented and discussed. The paper is concluded by numerical simulations that corroborate the theoretical analysis.

Keywords: Disturbance rejection (linear case), Observers for linear systems, Systems with time-delays
Abstract: A linear feedback control structure is proposed that allows internal model control design principles to be applied to unstable and marginally stable plants. The control structure comprises an observer using an augmented plant model, state estimate feedback and disturbance estimate feedback. Conditions are given for both nominal internal stability and offset-free action even in the case of plant-model mismatch. The Youla parameterization is recovered as a limiting case with reduced order observers. The simple design methodology is illustrated for a marginally stable plant with delay.

Keywords: Disturbance rejection (linear case), Linear systems, Optimal control theory
Abstract: In this paper, disturbance observer based quasi full information feedback control law of linear systems is proposed, where the feedback control law is with direct measurement of the plant states and the estimation of the disturbances. It shows that the system under control is input to state stable from the derivative of the disturbances to the system states provided that the given matching condition is satisfied. Furthermore, the effect of the disturbances can be compensated entirely if the disturbance is constant. The proposed scheme is verified by a simple numerical example and the stability control of four-wheel steering vehicle.

Keywords: Disturbance rejection (linear case), Optimal control theory, Time-varying systems
Abstract: This paper is concerned with a new framework called the kernel approximation approach to the L_1 optimal controller synthesis problem of sampled-data systems. On the basis of the lifted representation of sampled-data systems, which contains an input operator and an output operator, this paper introduces a method for approximating the kernel function of the input operator and the hold function of the output operator by piecewise constant functions. Through such a method, the L_1 optimal sampled-data controller synthesis problem could be (almost) equivalently converted into the discrete-time l_1 optimal controller synthesis problem. This paper further establishes an important inequality that forms the theoretical validity of the kernel approximation approach for tackling the L_1 optimal sampled-data controller synthesis problem.

Keywords: Disturbance rejection (linear case), Parametric optimization, Robust controller synthesis
Abstract: This paper emphasizes the design methodology for active tonal noise feedback cancellers starting from data collected on the system. To design such control systems, an accurate dynamic model of the system is necessary. Physical modeling can provide qualitative results but fails to yield enough accurate models for control design. The main point is concerned by the methodology is identification of primary path (noise propagation) and secondary path (compensation) models from data. The procedure is investigated in details starting with transfer functions’ order estimations, continuing with parameters estimation and model’s validation. The second aspect is the design of a noise canceller using the Internal Model Principle and the sensitivity function shaping in order to reduce the ”water-bed” effect. The estimated model’s quality for control design is illustrated by the experimental performance of a tonal noise feedback canceller implemented on a test bench.

Keywords: Structural properties, Disturbance rejection, UAVs
Abstract: In this paper, a structural feedback linearization technique is proposed. This is a quite simple and effective linear control scheme based on failure detection techniques. Our proposed linear control approach is intended to reject the nonlinearities, which are treated as failure signals affecting the systems dynamics. The proposed control methodology is illustrated via the attitude control of a quadrotor in hover flying.

Keywords: Control in economics, Equilibrium models
Abstract: Pricing control is an important problem in service systems. In this paper, we study a dynamic pricing and control problem in an open Jackson network. The goal is to determine the optimal admission prices and the optimal service rates at every state such that the long-run average welfare of the whole system is maximized. We prove that the original problem is equivalent to a rate-setting problem plus a price-setting problem. To solve the rate-setting problem, we derive a difference formula based on the sensitivity-based optimization theory. When the cost rate function is convex in service rates and the value rate function is concave in arrival rates, we decompose the rate-setting problem into a series of convex optimization subproblems. When the rate functions have linear structure, these subproblems are even simpler and a bang-bang control is optimal. For the price-setting problem, we determine the state-dependent prices so as to induce the optimal arrival rates obtained by the rate-setting problem. We propose a recursive algorithm to numerically compute the expected delays at every state. Finally, we conduct numerical experiments to explore properties of the optimal arrival rates, service rates, prices, and expected delays obtained by our approach.

Keywords: Computational social systems, Dynamic games
Abstract: In this paper, we discuss a stochastic sequential decision problem of optimally selecting the order in which to try n opportunities that may yield an uncertain reward in the future. The motivation came out from curiosity, after an informal conversation about what could be the best way to date friends. The problem structure turned out to be suitable also for other situations, such as the problem of optimally selecting the order of submission of a paper to journals. Despite the seemingly combinatorial nature of the problem, we show that optimal-tradeoff solutions can be found by simply ordering a sequence of n real numbers.

Keywords: Equilibrium models, Game theories
Abstract: This paper studies the distributed variational equilibrium seeking problem for a class of multi-coalition non-cooperative games via a continuous-time algorithm. In the multi-coalition game, there are multiple coalitions and each coalition (a virtual player in the game) is composed of a set of agents, which are actual decision makers for their coalition and cooperate to minimize the coalition payoff function. To solve this problem, the variational equilibrium seeking problem for a multi-coalition game is first formulated as a distributed variational inequality problem. Then, a novel distributed projected continuous-time algorithm, which is in the form of a projected ordinary differential equation, is proposed to solve the distributed variational inequality. Finally, the algorithm is applied to a numerical example to show the efficacy of the proposed algorithm.

Keywords: Financial engineering, Computional intelligence for business and economy, Financial systems
Abstract: This paper is devoted to the derivation of a new approach for discovering mutual fund holdings from returns only. The approach is based on the use of a discrete-time asymptotic observer with forgetting factor which allows to estimate on-line both the holdings and the holding changes in portfolios. A realistic example is also provided, which demonstrates the effectiveness of the proposed approach. To the best of our knowledge, such an approach has never been proposed before.

Keywords: Financial systems, Financial engineering, Financial dynamics
Abstract: Control of drawdown, that is, the control of the drops in wealth over time from peaks to subsequent lows, is of great concern from a risk management perspective. With this motivation in mind, the focal point of this paper is to address the drawdown issue in a stock trading context. Although our analysis can be carried out without reference to control theory, to make the work accessible to this community, we use the language of feedback systems. The takeoff point for the results to follow, which we call the Drawdown Modulation Lemma, characterizes any investment which guarantees that the percentage drawdown is no greater than a prespecified level with probability one. With the aid of this lemma, we introduce a new scheme which we call the drawdown-modulated feedback control. To illustrate the power of the theory, we consider a drawdown-constrained version of the well-known Kelly Optimization Problem which involves maximizing the expected logarithmic growth of the trader’s account value. As the drawdown parameter dmax in our new formulation tends to one, we recover existing results as a special case. This new theory leads to an optimal investment strategy whose application is illustrated via an example with historical stock-price data.

Keywords: Control in economics, Climate change, Economic models
Abstract: Integrated assessment models (IAMs) are a key tool in studying the interdependence of the global economy and the climate system. For example, the dollar value of carbon dioxide emissions due to anthropogenic climate damages, known as the social cost of carbon (SCC), can be computed using the widely used DICE (Dynamic Integrated model of Climate and the Economy) IAM by solving an open-loop optimal control problem. The results of such an open-loop decision-making strategy, however, do not fully reflect the impacts of uncertainty in the dynamic response of the global climatic system to radiative forcing. In this paper, we propose an implementation of the DICE IAM based on model predictive control (MPC). This MPC-based approach draws a clear distinction between the climate model used by DICE for mitigation planning purposes, and the ``true'' global climate herein captured by a low-order emulation of a model drawn from a state-of-the-art climate model ensemble (CMIP5, the fifth phase of the Coupled Model Intercomparison Project). The closed-loop control methodology in this paper thereby quantifies the impact of parametric climate model uncertainty (plant--model mismatch) on estimates of the SCC obtained from DICE.

Keywords: Hybrid and alternative drive vehicles, Modeling, supervision, control and diagnosis of automotive systems, Vehicle dynamic systems
Abstract: Anti-jerk controller are essential for drive comfort during load-changes, since they reduce undesired driveline oscillations. Hybrid electrified vehicles enable greater degree of freedom to control these oscillations due to the two actuators, namely internal combustion engine and electric machine. At the same time the more complex communication structure of electronic control units in a hybrid electrified vehicle inserts more time delay in the system, which can cause instability in driveline oscillation control. This paper analysis the effect of dead time on a parallel hybrid electrified driveline model with proportional feedback controller and present a feedforward and feedback strategy with dead time compensation. Simulation results of the proposed approach show good step-response behavior and robustness against dead time.

Keywords: Vehicle dynamic systems, Automotive system identification and modelling, Modeling, supervision, control and diagnosis of automotive systems
Abstract: The aim of this study is to quantize the effectiveness of actuating on bicycle braking dynamics with focus on critical rectilinear maneuvers, namely wheel-lock and nose-over. Those situations define the physical limits of rectilinear bicycle braking. The objective is to gain a profound understanding of the limits of unactuated vehicle dynamics by creating a multibody model that is parametrized and validated via an instrumented bicycle. A good model accuracy is obtained modeling the rider as a point mass located at the experimentally determined center of gravity and using tire slip curves which are also gained by experiment. In the second step the obtained system knowledge is used to evaluate possible ways of actuation, which on the one hand could assist the rider not to leave the limits of braking dynamics and on the other hand extend them. Researched types of actuation are brake torque modulation, suspension fork locking as well as active manipulation of the riders center of gravity by a seatpost actuator.

Keywords: Automotive system identification and modelling, Kalman filtering techniques in automotive control, Automotive sensors and actuators
Abstract: Electric Parking Brake system is the automation of traditional manual vehicle parking brake system. Naturally, an automation in automotive field includes an improvement of vehicle features that will become, in short time, normal production functionalities, since forever present on vehicles for users. Certainly, EPB control design is a robust design which is influenced by a series of different plant disturbances / uncertainties as: available vehicle battery voltage, ambient/caliper/pad temperatures, vehicle harness length, and normal braking hydraulic pressure already present on caliper spindle. Furthermore EPB system performance has a big impact on vehicle safety, ECE 13H braking legislation part related to parking brakes prescribes many different performance thresholds, static and dynamic, that are easily translated in low level control design requirements. In this context, paper presents: an algebraic mathematical model, including linear and non-linear parts of braking motor-on-caliper, dynamic response identification of electric parking brake actuator by least square identification experiments, linearization of identified non-linear model, observability analysis of identified linear model, and control of Motor on Caliper (MoC) validated with MIL and HIL approach. The simulation and experimental results on a motor-on-caliper test bench, carried out in FIAT Research Center, show that designed identified model is well matched with the clamping force actuator, and it can be useful in drive away robust control logics development.

Keywords: Kalman filtering techniques in automotive control, Nonlinear and optimal automotive control, Automatic control, optimization, real-time operations in transportation
Abstract: Highly automated driving is one of the major visions in automotive research. Low speed autonomy like e.g. highly automated parking is an essential intermediate step towards that goal. While lateral control for automated parking is already successfully implemented in series production, major disturbances such as curbs still challenge longitudinal control. This paper proposes a disturbance estimator that is capable of estimating external resistance forces which originate from the road and impair longitudinal vehicle control in low speed maneuvers. Particularly, we will focus on curbs that can be treated as a road resistance force. The underlying nonlinear model is derived from an extended single-track model. The model accounts for lateral front tire forces which have a major impact on longitudinal vehicle dynamics at large steering angles. In a simulation study, we evaluate the estimator performance in five reverse driving on-curb parking scenarios and demonstrate that road resistance can be estimated appropriately.

Keywords: Man-machine interfaces, Vehicle dynamic systems, Intelligent driver aids
Abstract: Bicycles are seen as a viable solution to a number of transportation problems: the use of bicycles alleviate congestion, reduce pollution and generally improve public health. In many congested cities, bicycles are the quickest means of transportation. The widespread use of bicycles, along with the exponential growth of electrically power assisted bicycles, could pose some safety issues related to reckless cycling. This paper presents an inertial measurement based approach to quantify and classify different riding styles. The bicycle velocity, longitudinal acceleration and angular rates are monitored to identify dangerous riding from three different perspective: longitudinal, vertical and lateral dynamics. In this way, it is possible to assign a number assessing the quality of each trip. The approach is developed using data collected from ad-hoc experiments and real-world data.

Keywords: Automatic control, optimization, real-time operations in transportation, Nonlinear and optimal automotive control, Modeling and simulation of transportation systems
Abstract: Automatic Train (or Tram) Operation (ATO) systems have been widely studied in the last decade to guarantee energy-efficient operation. Speed profiles and timetables are derived by applying optimization methods geared towards finding the optimal trade-off between journey time, emissions and energy efficiency. An on-board controller is required to track such optimal trajectories, despite model uncertainties. Due to the non-linear dynamics and non-holonomic constraints on both inputs and outputs, the tracking of such trajectories represents a difficult task. This paper deals with the design of the tracking algorithm, for a catenary-free tram, with regenerative braking. A Model Predictive Control (MPC) approach is used to track the optimal position profile. Thanks to the ability to easily deal with multivariable systems, make use of the references and disturbances preview, and enforce time-varying input/output constraints, MPC is the perfect candidate to fulfill this task. The proposed MPC formulation is simple enough to be implemented in real-time, with a quadratic programming solver. Results show that the algorithm tracks the position reference, and guarantees punctuality and comfort, while enforcing time-varying constraints on vehicle velocity, electrical machine and friction forces. The results are also confirmed in the case of operation with model mismatch.

Keywords: FDI for linear systems, Observer based and parity space based methods for FDI, Signal processing for FDI
Abstract: The paper proposes a way to derive analytical redundancy relations for diagnosable systems. The starting point is an over-determined set of constraints, which is obtained by a structural analysis of the system under consideration. The problem is to find an analytical expression for the residuals in terms of the measured signals and the derivatives of these signals. A new method is described that replaces the derivative constraints included in the structure graph by a series of equations that represent the derivatives of the state variables and the outputs. The result is a matrix-vector representation of an over-determined set of equations, which leads to the analytical redundancy relations. The method is illustrated by an example.

Keywords: Distributed Fault Diagnosis
Abstract: In this paper, a novel framework is proposed for deadbeat distributed Fault Detection and Isolation (FDI) of large-scale continuous-time LTI dynamic systems. The monitored system is composed of several subsystems which are linearly interconnected with unknown parameterization. Each subsystem is monitored by a local diagnoser based on the measured local output, local inputs and the interconnection variables from the neighboring subsystems. The local FDI decision is based on two non-asymptotic state-parameter estimators using Volterra integral operators which eliminate the effect of the unknown initial conditions so that the estimates converge to the true value in a deadbeat manner and therefore the fault diagnosis can be achieved in finite time. Moreover, the unknown interconnection parameters and the unknown fault parameters are simultaneously estimated. Numerical examples are included to show the effectiveness of the proposed FDI architecture.

Keywords: Observer based and parity space based methods for FDI, FDI for linear systems, Computational methods for FDI
Abstract: In this paper, a robust parity relation approach is proposed for fault detection of linear systems with the probabilistic time-invariant parameter uncertainties. To deal with polynomial dependence on uncertain parameters, a set of parity relations with polynomial parameterization is derived to decouple the unknown initial condition. Due to the polynomial structure having a finite degree, the generated residual vector admits an exact polynomial chaos expansion with the same degree, which enables an efficient online computation for quantifying the moments of the residual vector. The obtained moments are used to calculate a confidence set for checking the consistency between the observed system behavior and the parity relations. Compared to deterministic robust parity relation methods, the employment of probabilistic information allows a less conservative way to ensure a low false alarm rate while maintaining a high fault detection rate. In contrast to the existing polynomial chaos-based fault diagnosis literature, our proposed approach avoids truncation errors of polynomial chaos expansions, and is less computationally demanding. The proposed approach is illustrated by using a two-tank system example.

Keywords: FDI for nonlinear Systems, Statistical methods/signal analysis for FDI
Abstract: The principal component analysis (PCA) is a linear technique widely used to retrieve a subspace that maximizes the variance of the data, making the presence of a fault easy to detect. Nevertheless, the real systems are nonlinear. To this end, we propose in this paper to use a kernel-based technique known as kernel principal component analysis (KPCA) for fault diagnosis. The main idea is to use a nonlinear transformation that projects data into a higher dimensional feature space, where conventional PCA is applied. Although detection can be defined in this space, the estimation of the fault requires the map back to the input space. In this sense, we derive an iterative pre-image technique. A study on possible initial points is done. Three initialization techniques based on different properties are presented. The relevance of the proposed technique is illustrated on simulated data.

Keywords: Active Fault Diagnosis, FDI for linear systems, Structural analysis and residual evaluation methods
Abstract: This paper presents a methodology for model based robust fault diagnosis and a methodology for input design to obtain optimal diagnosis of faults. The proposed algorithm is suitable for real time implementation. Issues of robustness are addressed for the input design and fault diagnosis methodologies. The proposed technique allows robust fault diagnosis under suitable conditions on the system uncertainty. The designed input and fault diagnosis techniques are illustrated by numerical simulation.

Keywords: AI methods for FDI, Condition Monitoring, FDI for hybrid systems
Abstract: This paper deals with the problem of improving data-based diagnosis of continuous systems taking advantage of the system control information represented as discrete event dynamics. The approach starts from dynamic clustering results and, combining the information about operational modes, automatically generates a discrete event system that improves clustering results interpretability for decision-making purposes and enhances fault detection capabilities by the inclusion of event related dynamics. The generated timed discrete event system is adaptive thanks to the dynamic nature of the clusterer from which it was learned, namely DyClee. The timed discrete event system brings valuable temporal information to distinguish behaviors that are non-diagnosable based solely on the clustering itself.

Keywords: Perception and sensing, Information and sensor fusion, field robotics
Abstract: This paper presents a sensing method to measure three-dimensional (3-D) information in an underwater environment using an acoustic camera. Acoustic cameras can acquire clear information even in turbid water which is difficult to photograph with an optical camera. In addition, its detection range is extensive. Compared to traditional sensors, acoustic cameras with no restrictions on vision are the most powerful sensors for acquiring underwater information. In this paper, we propose a novel approach which enables 3-D measurement of underwater objects using arbitrary viewpoints based on an extended Kalman filter (EKF). By using the probabilistic method based on the EKF, 3-D reconstruction of underwater objects is possible even if the control input for camera movement has uncertainty. Furthermore, since the EKF based estimation is performed sequentially each time, our methodology can be adapted to real-time applications. Simulation and experimental results show the effectiveness of the proposed method.

Keywords: Mobile robots, field robotics, Application of mechatronic principles
Abstract: In this paper, we propose a flexible pneumatic robot that can move in narrow pipes and across open spaces. The development of such robots is necessary to access the insides of Fukushima Daiichi Nuclear Power Plant's pressure containment and reactor pressure vessels via pipes and to then move around inside and investigate damage to them. The novel flexible pneumatic robot proposed here realizes both in-pipe and open-space locomotion by integrating expansion-type pneumatic hollow-shaft actuators and a pneumatic rotary steering mechanism. We first present the propulsion and steering concept of the proposed robot before detailing its design and the developed prototype. Further, we present the experimental evaluation of the characteristics and performances of the steering and propulsion mechanisms. We found that each expansion-type pneumatic hollow-shaft actuator can generate a maximum traction force of 11 N, whereas the steering mechanism generates a maximum steering force of 0.55 N. Finally, we conducted a trial to confirm the locomotion performance of the robot both in pipes and across open space. The results show that the robot can smoothly touch down into open space from high-lying pipes and subsequently move around across open spaces toward a given coordinate.

Keywords: Robots manipulators, Motion Control Systems, field robotics
Abstract: The joint position constraints of two cooperative robotic manipulators is a critical issue, especially when both manipulators grasp and move the same object. When two robots cooperate to move a grasped object from one point to another one, following a desired path, the general solution adopted in the literature is to use the kinematic redundant motions possessed by a single manipulator, in order to avoid joint constraints. However, this method requires a number of redundant motions at least equals to the number of joint constraints to satisfy. The study proposed in this paper is focused on developing a joint position limits avoidance strategy, which is able to satisfy all joint limits even when the number of redundant motions are no longer sufficient to ensure them with a classical approach.

This is achieved by means of a supervisory control system, which allows to locally and temporary change the desired end-effectors motion, when the redundant motions are not available. The supervisory control sacrifices the path following task in order to ensure joint position limits avoidance, while preserving at the same time the relative end-effector motion, such that the stability of the grasped object is still ensured. The proposed strategy has been implemented on a commercial robot, namely Baxter, which possesses two anthropomorphic arms having one degree of redundancy. The obtained results prove the effectiveness of the proposed supervisor controller, which can be easily extended and applied in manufacturing scenario. Whereas path following can be temporary sacrificed (e.g., the grasped object can be moved from one point to another in different ways), the joint constraints must be strictly preserved for safety reasons.

Keywords: Information and sensor fusion, Autonomous robotic systems, Multi sensor systems
Abstract: The development of remote and quick radiation imaging methods for high dose-rate environments is important to accelerate decommissioning of the Fukushima Daiichi Nuclear Power Station (FDNPS). We have developed a remote radiation imaging system consisting of a Compton camera and a multicopter-type drone to remotely measure the distribution of radioactive contaminations inside FDNPS. In outdoor performance evaluation tests in the coastal areas of Fukushima, Japan, the drone system succeeded in observing several hotspots from the sky. In addition, we are developing technology for simultaneous localization and mapping of remotely operated machines such as drones to complete the remote radiation imaging system, which can fly autonomously.

Keywords: Mobile robots, Telerobotics
Abstract: To survey a water leakage of Unit 3 Primary Containment Vessel hatch in Fukushima Daiichi Nuclear Power Station, a small remotely-operated robot was developed. On this robot a smartphone was mounted as a camera and its body was manufactured by a 3D printer. The feature of this robot is that it has the abilities to pass a narrow crank aisle and to climb a 50 mm high-step which is about half as tall as its overall height. This step-climbing ability resulted from the mount of a climbing nose and a supporting tail, in addition, an improvement of crawlers.

Keywords: Flying robots
Abstract: We study the control problem of an aerial vehicle moving in the 3D space and connected to an independently moving platform through a physical link (e.g., a cable, a chain or a rope). The link is attached to the moving platform by means of a passive winch. The latter differs from an active winch by producing only a constant uncontrollable torque. We solve the problem of exact tracking of the 3D position of the aerial vehicle, either absolute or with respect to the moving platform, while the platform is independently moving. We prove two intrinsic properties of the system, namely, the dynamic feedback linearizability and the differential flatness with respect to the output of interest. Exploiting this properties we design a nonlinear controller able to exponentially steer the position of the aerial robot along any sufficiently smooth time-varying trajectory. The proposed method is tested through numerical simulations in several non-ideal cases.

Keywords: Logistics in manufacturing, Production planning and control, Procedures for process planning
Abstract: Value Stream Mapping is a critical tool when it comes to implement the lean approach, it has spanned to many sectors in industry. Although previous studies justify its use in the manufacturing sector by identifying previous cases in the literature, none to the best of our knowledge has used our approach to explore the aspects of this review, yet the potential exists. Based on a systematic approach, we analyzed available literature published in refereed journals, providing valuable findings related to the evolution, application and performance of the Value Stream Map in context of the manufacturing sector. We could realize of important information about the trends in regards of the research in this field that is carried out over the world, the application of VSM in its traditional version as well as the variants of it, the key aspects analyzed by authors (i.e. performance indicators considered). To sum up, we provide the scientific comunity with valuable insights of the application of Value Stream Mapping; this may serve as a mean to explore further the ways in which this tool is developed in both, the manufacturing sector as well as other areas of knowledge.

Keywords: Modeling of manufacturing operations, Production planning and control, Intelligent manufacturing systems
Abstract: Manufacturing systems with waiting time constraints are widely observed in battery, automotive, semiconductor, food and many other industries. Under such requirements, the residence time of a part in a buffer is constrained by a given time limit and the part exceeding the limit is subject to quality inspection and could be scrapped with a certain probability. To evaluate the performance of such systems, in this paper, we study Bernoulli serial lines with waiting time limits. For two-machine lines, analytical formulas are derived to predict the system performance in the steady state. In case of longer lines, direct analysis is intractable, thus an iterative aggregation procedure is presented. Based on extensive numerical experiments, the convergent results of the procedure can be obtained and high precision in performance estimation is achieved.

Keywords: Modeling of assembly units, Assembly and disassembly, Modeling of manufacturing operations
Abstract: As a common production practice in many developing economies and in reverse logistics, unbalanced merging assembly lines are a research area which is re-emerging in prominence. This paper aims to study the performance of reliable, unpaced merging assembly lines that are unbalanced in terms of their buffer storage sizes. Lines are simulated with different values of line length, mean buffer storage capacity and configuration of uneven buffer allocation. The best patterns in terms of generating higher throughput and lower average buffer level as compared to a balanced merging line are those where total available buffer capacity is allocated as evenly as possible between workstations and to concentrate more buffer capacity towards the end of the line, respectively.

Keywords: Job and activity scheduling, Modeling of manufacturing operations, Discrete event systems in manufacturing
Abstract: This paper focuses on a three-machine flow shop scheduling problem with overlapping waiting time constraints for makespan minimization. In the problem, waiting times of each job between the first two machines and between the first and the third machines are constrained by two distinctive time limits, respectively. Such overlapping waiting time constraints are one of the common scheduling requirements in semiconductor manufacturing since up to 20% of whole process steps are controlled with waiting time limits for better quality. For example, wafers completed with chemical treatment on a diffusion machine must be cleaned on a cleaning machine and then should be processed on another diffusion machine. In this case, two waiting time limits are independently applied between diffusion and cleaning machines and between two diffusion machines, respectively. We first identify several dominance properties of the problem and develop a branch and bound algorithm using the properties. We use heuristic algorithms to obtain a good initial solution and derive five different lower bounds. Computational tests are performed for evaluating the performance of the algorithm.

Keywords: Logistics in manufacturing, Complex logistic systems, Modeling of manufacturing operations
Abstract: Carrousels are Automated Storage and Retrieval Systems frequently used in warehouses. The performance of warehouses with carousels is highly correlated to the order picking performance. The order picking is one of the main activities in warehouse management, consisting of retrieving the required products from their storage location to fulfil customers’ orders. A critical operation for the order picking efficiency is the order batching, namely how to regroup customers’ orders into batches before the collect. In this paper, we provide an optimization model for the orders batching problem, when the products are stored in one or several carrousels, with the objective of minimizing the total completion time. We use real data in order to test and validate our models. Numerical experiments show that our model outperforms the actual batching strategy used by the considered companies.

Keywords: Discrete event systems in manufacturing, Modeling for control optimization
Abstract: Optimization is an important area of research. As many optimization problems are NP-Hard it is important to have a wide selection of heuristic approaches to choose from when solving problems. Prioritized planning is a technique traditionally used to solve the multi-robot path planning problem. In this paper we investigate how incremental scheduling, a technique based upon prioritized planning can be applied to scheduling wafer processing in a cluster tool. For our test case our approach is shown to be capable of finding a plan of equal quality to other scheduling approaches and thus suggests our method could be of potential use in other manufacturing/material handling applications.

Keywords: Kalman filtering techniques in marine systems control, Dynamic positioning
Abstract: Dynamic Positioning systems usually include wave filtering functionalities to reduce control actions, and hence wear and tear of the propulsion system. Different approaches for design and parametrization of the wave filter components have been investigated through the years. Anyhow, tuning of these algorithms, especially Kalman filter, is a crucial task for proper function, but systematic guidelines or algorithms are not existing. This paper proposes a method for auto-tuning of Kalman filter for dynamic positioning using adaptive algorithms. Existing methods of residual- and innovation-based estimation of covariance matrices are incorporated into a linear Kalman filter and evaluated in the simulations. The resulting tuning shows significant improvements in state estimation.

Keywords: Dynamic positioning
Abstract: To ensure a ship's capability of Dynamic Positioning, the propulsion system of the vessel is usually designed over-actuated, meaning the ship has more control inputs than degrees of freedom. The distribution of thrust demands of some higher-level controller onto the available thrusters, the thrust allocation, is an essential problem and due to the over-actuated problem not trivial. Furthermore, the thrust should be delivered fast and precisely to achieve good positioning results. The usage of a Voith Schneider Propeller allows thrust generation with these properties but spans a new problem due to complex modeling and increased number of control variables. The paper on hand presents a thrust allocation algorithm which utilizes the dynamic advantages of Voith Schneider Propeller. The algorithm uses a two-step approach consisting of Quadratic Programming optimization and external tracking algorithms for calculation of power- optimal propulsion control. The proposed strategy is compared to a non-linear, multi-criteria optimization which is based on high quality open-water characteristics. The results show strong similarities though the computational time can be significantly reduced by usage of the proposed allocation.

Keywords: Dynamic positioning, Marine system navigation, guidance and control, Kalman filtering techniques in marine systems control
Abstract: This paper presents design and evaluation of a wavelet-based multi-resolution proportional integral derivative controller (MRPID) for dynamic positioning (DP) of vessels under noise and environmental disturbances. In the proposed MRPID controller, the errors with position and orientation are decomposed into different frequency components using discrete wavelet transform. The different scales of wavelet transform represent details such as external disturbances, process noise and measurement noise. A set of sub-PID controllers are assigned to selected error components and the commands generated by all controllers are added together to produce the control command for dynamic positioning of the vessel. Performance of the controller is evaluated in simulation and compared with the conventional PID controller with acceleration feedback. Nonlinear state estimation is performed by using an unscented Kalman filter assuming a typical sensor package for localization.

Keywords: Marine system navigation, guidance and control, Autonomous surface vehicles, Map building
Abstract: The planning of mining operations in water filled open-pit mines requires detailed bathymetry to create a mine plan and assess the involved risks. This paper presents post-processing techniques for creating an improved 3D model from a survey carried out using an autonomous surface vehicle with a multibeam sonar and a GPS/INS navigation system. Inconsistencies of the created point cloud as a result of calibration errors or GPS signal loss are corrected using a continuous-time simultaneous localization and mapping (SLAM) solution. Signed distance function (SDF) based mapping is employed to fuse the measurements from multiple runs into a consistent representation and reduce sensor noise. From the signed distance function model we reconstruct a 3D surface mesh. We use this terrain model to establish a virtual reality scene for immersive data visualization of the mining operations for testing and planing during development. Results of the proposed approach are demonstrated on a dataset captured in an abandoned submerged inland mine.

Keywords: Autonomous underwater vehicles, Autonomous surface vehicles, Marine system navigation, guidance and control
Abstract: Position estimation is one of the important tasks to achieve vehicle autonomy. This article addresses the problem of estimating the position of the center of mass of a vehicle from range measurements that are available to the vehicle via communication of its on-board receiver to multiple beacons. The model proposed in this article assumes that the true distance between a beacon and the on-board receiver in the vehicle is at most equal to a predetermined function of the measured distance. This predetermined function is computed offline while calibrating the beacons, and an algorithm to update this function adaptively with online measurements is also presented. These range measurements are further utilized to estimate the position of on-board receiver, and hence the vehicle, by solving a convex optimization problem. The position is essentially an analytical center of the set of all possible locations of the receiver. Several simulations and experiments also corroborate the effectiveness of the proposed approach.

Keywords: Marine system navigation, guidance and control, Control architectures in marine systems, Motion control
Abstract: This paper addresses the design and implementation of a high-level control system for the planning, supervision, and mission operation of the observation class remotely operated vehicle Visor3. This work was developed following the Waterfall Model for the Software Development Life Cycle. First, a brief description of the ROV Visor3 is made. Then, the functional and non-functional requirements, that are based on the ISO/IEC/IEEE 29148:2011 and were gathered from stakeholders, are presented. The software architecture is based on the standard ISO/IEC/IEEE 42010:2011. The implementation of the high-level control system is done by using a high-level programming environment using layers through parallel executed threads. This architecture establishes a flexible structure and guidelines that orient the implementation of future components for the mission control system of the ROV.

Keywords: Fault diagnosis and fault tolerant control, Model predictive and optimization-based control, Fault accommodation and Reconfiguration strategies
Abstract: Fault-tolerant control is important for the autonomous operation of complex processes. When model predictive control is used, fault detection and diagnosis is often based on the available process model used by the controller. Unanticipated faults can however cause misdiagnosis of faults, and consequently incorrect compensation actions. A fault-tolerant model predictive controller is presented in this article and tested on a grinding mill circuit simulator. The fault diagnosis algorithm quickly and accurately detects anticipated faults based on the generalized likelihood ratio test. Unanticipated faults are isolated when the process data do not sufficiently match the most probable anticipated fault data. The scheme is applicable to nonlinear multiple-input multiple-output systems.

Keywords: Process control applications, Model predictive and optimization-based control, Monitoring of product quality and control performance
Abstract: The grinding process in ball mills is notoriously known to be highly inefficient: only 1 to 2% of the inputted electrical energy serves for creating new surfaces. There is therefore obvious room for improvement, even considering that the dominant impact mechanism in tumbling mills is a fundamental liability limiting the efficiency. Moreover, the Coalition for Energy Efficient Comminution estimates that on average, 53% of the energy consumed on a mine site is for comminution processes. This represents a tremendous leverage to generate economical and environmental benefits. This paper analyses how properly designed control systems allow a better usage of the capacity of grinding circuits, and a reduction of the specific energy consumption even without modifying the product size target. Three different control systems are compared: (1) typical basic regulatory, (2) regulatory control of the product size attribute, and (3) model-based predictive control. Simulation results show that controlling the product size distribution attribute ensures not only meeting downstream requirements and avoiding overgrinding, but also allows processing higher throughput in some instances, hence decreasing the specific energy. The discussion highlights the pros and cons of all three scenarios.

Keywords: Process observation and parameter estimation, Measurement and instrumentation, Advanced process control
Abstract: This study aims to indicate which measurements are required in order to estimate the volume of rocks and balls in a semi-autogenous grinding mill as two separate states. The nonlinear observer model used here includes the following process states: water, solids, rocks, and balls in the mill, where solids are all ore small enough to discharge through the end-discharge grate, and rocks are all ore too large to discharge. The model includes the discharge rate, abrasion rate of rocks and the abrasion rate of balls as parameters. The available measurements are the total mill filling, the discharge flow-rate, and the discharge density. As seen from an observability analysis, the states and parameters become observable from the second-order time-derivatives of the measurements. The minimum set of measurements required for observability of all states and parameters is the mill filling, the discharge density, and the first and second-order time-derivatives of the discharge density. However, modelling the second-order time-derivative of the discharge density is problematic, as it assumes constant model parameters. Although the combined volume of rocks and balls can be estimated using measurements of the mill filling, the discharge flow-rate, the discharge density, and the first-order time-derivative of the discharge density, these measurements remain insufficient to distinguish between rocks and balls. To reliably distinguish between the rocks and balls in the mill, an additional measurement apart from the ones mentioned above is required. Since power draw models introduce large parameters sets of their own, another viable and reliable option is required.

Keywords: Measurement and instrumentation, Identification and modelling, Monitoring of product quality and control performance
Abstract: Online measurement of particle size is vital to the development of advanced control systems for comminution processes. Horizontal stirred mills, such as the IsaMill, are designed for more efficient ultrafine grinding and have made significant inroads in the mineral processing industries since their introduction more than a decade ago. Despite their energy efficiency, significant improvement is possible via more efficient control of these mills. Advanced control generally requires online information on the key performance variables of the mill. In this regard, measurement of the particle size in the mill is problematic. However, this problem can be addressed by use of soft sensors, whereby the particle size can be estimated from the measurements of other process variables. In this investigation, such a soft sensor is developed for online estimation of particle size on an industrial IsaMill in Western Australia. The sensor consists of a random forest model that uses operational variables measured online as predictors to estimate the P80 particle size of the mill. Principal component analysis is used in conjunction with the random forest to enable it to assess the similarity of new process measurements to the data in its training data base. When the new data exceed a Hotelling’s T2 or a prediction error or Q-index threshold, recalibration of the model is automatically performed.

Keywords: Identification and validation, Clinical trial, Artificial pancreas or organs
Abstract: In the last years, Model Predictive Control (MPC) proved to be one of the most promising approaches for an Artificial Pancreas (AP), a device for closed-loop blood glucose control in subjects affected by Type 1 Diabetes (T1D). MPC performance is highly influenced by the quality of the model used for prediction. Moreover, the inter-patient variability characterising subjects with T1D increases the need of patient-tailored models. Recently, promising results have been obtained in silico using the UVA/Padova simulator in cite{Soru:2012} and cite{Messori:2016} where different individualization techniques have been studied and compared to the ``average" model of the UVA/Padova adult population showing significant improvements in term of prediction ability. The aim of this paper is to verify the applicability of the technique described in cite{Soru:2012} and extend it to be used on free-living data collected without ad hoc clinical protocols. Data were collected during a 1 month trial in free-living conditions (cite{Renard:2016}). In this proof-of-concept case study, individualized models obtained with different identification parameters are compared with the ``average'' model that was used to synthetize the MPC controller used during that trial. The individualized models show superior prediction performance and prove robustness to non-optimal algorithm initialization in a selected test-case.

Keywords: Manufacturing plant control, Interoperability and sustainable enterprise, Intelligent manufacturing systems
Abstract: The manufacturing industry is under pressure to improve performance in a number of areas including productivity, efficiency and environmental impact. Knowledge-driven performance improvements require not just access to data but to detailed contextual information that is specific to the individual plant and its process implementation. In meeting this requirement there is often a trade-off when selecting between customising the generic offering of a commodity application and the flexibility of a custom-developed application.

This paper describes a unique information system, developed on the G2 real-time intelligent system platform, that has been specifically designed to facilitate performance improvements of a large brewery.

Keywords: Discrete event systems in manufacturing, Manufacturing plant control, Efficient strategies for large scale complex systems
Abstract: In Supervisory Control Theory (SCT) of Discrete Event Systems, event refinements have been exploited to simplify modeling tasks. Approximations complement this approach with an alternative to also reduce synthesis effort. In spite of advantageous to modeling and synthesis, refinements do not directly cover the SCT implementation phase. In general, controllers obtained with and without refinements are expected to be implemented with equivalent hardware cost. This paper presents a decentralized architecture that extends the gains provided by refinements from synthesis to implementation. The approach separates supervisor and distinguisher codes in two different structures, which then communicate in such a way that the resulting control action on the plant is equivalent to the centralized case, while the implementation is conducted with hardware savings. An example of a buffering system illustrates the approach.

Keywords: Discrete event systems in manufacturing, Dependable manufacturing systems control, Manufacturing plant control
Abstract: This work deals with safe control of manufacturing systems controlled by Programmable Logic Controller (PLC). The used approach is based on Boolean safety constraints, which act as a safety filter, in order to guarantee the safety on-line. To ensure the safety whatever the inputs and the control program, the safety constraints must be checked formally. In this paper we proposed a formal off-line approach to check and ensure the consistency. The approach uses graph representation of the safety constraints. Thanks to graph algorithm, it is possible to detect inconsistencies and to help designers to solve them. Previous works on safety filter have proposed necessary conditions but none sufficient conditions, in this paper we proposed a necessary and sufficient condition to check and ensure the consistency. The safety filter approach and the consistency problem are presented. Then the approach is illustrated and a discussion around the application to manufacturing system is done.

Keywords: Manufacturing automation over networks, Maintenance models and services, Intelligent maintenance systems
Abstract: Analysis of computer networks is essential for the design and maintenance of complex production systems, especially considering the increasing interconnection by Internet of Things devices. An important tool in network analysis is the visualization of network topologies. In this work we present a novel approach for automatic visualization of rooted trees that includes port specific connections as they appear in communication networks. We further show how this algorithm improves the drawing of industrial Ethernet network topologies, taking also problematic properties such as cycles and multiple root nodes into account.

Keywords: Assembly and disassembly, Modeling of assembly units, Modelling and decision making in complex systems
Abstract: Lead times and their variability have a key role to play in multi-component assembly systems, directly impacting their performance. They are rarely known exactly in advance, being generated by various sources of uncertainty. This paper focuses on the stochastic solution of the replenishment planning problem of a single-level assembly system, in the framework of which: (i) procurement decisions must be made before the fixed demands are realized, and (ii) backlogs are authorized. By approaching this problem in the context of stochastic programming, we discuss the value of (stochastic) information for, subsequently, characterizing and better apprehending the grade of stochasticity and its effect on the corresponding solutions.

Keywords: Assembly and disassembly, Modeling of assembly units
Abstract: Multi-manned assembly lines are usually found in plants producing large-sized high-volume products such as automotive sector. In this paper, the balancing problem of this kind of assembly lines with skilled workers is addressed. A new mixed integer programming formulation is presented to solve this problem optimally with the objective of minimizing the total operating cost of the assembly line. The main advantage of the proposed model is to allow the workers in each multi-manned workstation to perform the different assembly tasks of same product simultaneously. The proposed formulation has been used to solve some experimental problems found in the literature. The comparison between the results obtained with the proposed model and those obtained with the model proposed by Moon, Logendran, and Lee (2009) shows that the proposed model can improve the operating cost of the system by reducing the number of workers and workstations.

Keywords: Flexible and reconfigurable manufacturing systems, Modeling of assembly units, Production planning and control
Abstract: Today’s industrial automation production systems (aPS) have to deal with the requirement to produce many different products. Hence, not only a flexible modularized aPS, but also a flexible modularized material flow system (MFS) is required in order to improve modifications to the system, i.e. extending, reducing or modifying parts of the system. To reduce the engineering effort involved in setting up modularized MFS and to deal with the complexity of the system, a definition of the architecture, behavior and (pre)defined interfaces of such modules is provided by a model-based engineering approach, which is based on an appropriate meta model. In addition, the modules can interact with neighboring modules to execute specific logistic functionalities by using (pre)defined interfaces. Since the development of automated material flow modules (aMFM) can be considered as an evolutional process, the aMFM are frequently redeveloped using current-state-of-the-art technologies, which, in some circumstances, also require newly-developed interfaces. Thus, an inconsistency between older and newer modules containing different version numbers occurs when different (pre)defined interfaces are used, leading to a reduction in system flexibility. To implement the backward compatibility of newer modules, the (pre)defined software interfaces of the newer modules, or even of its neighboring modules, usually have to be adapted manually, which increases the engineering effort and propensity for errors. This paper introduces a model-based approach to analyzing and adapting the interface of aMFM automatically in order to enable interaction with neighboring modules across different version numbers, i.e. modules containing a different version number. Hence, the engineering effort and error-proneness for the adaption can be reduced. For instance, the automatic analysis and adaption of the interface of a newly-developed QR Code Scanner is considered here.

Keywords: Flexible and reconfigurable manufacturing systems, Discrete event systems in manufacturing, Production planning and control
Abstract: This paper investigates the relevance of modeling workshop scheduling problems using a Discrete Event System (DES) approach based on timed automata (TA). To realize this study, the DES approach is compared with a classical approach based on Mixed Integer Linear Programming (MILP). In order to compare these two modeling approaches, an evaluation system is proposed based on: (i) a problem instances generation system using the classical three-fields notation of Graham (alpha|beta|gamma), (ii) a measurement system based on three criteria and associated metrics: complexity, genericity and scalability. This system covers the most common problems when dealing with flexible workshop scheduling. The results obtained by the application of the method is finally discussed.