Keywords: Guidance navigation and control, Robotics technology, Modeling
Abstract: The inspiration for snake robots comes from biological snakes. Snakes can move over virtually any type of terrain, including narrow and confined locations. They are good climbers, very efficient swimmers, and some snakes can even fly by jumping off branches and using their body to glide through the air. In this plenary talk we will review recent results on modelling, analysis and control of snake robots. The talk will also describe a new research direction within snake robotics, where underwater snake robots are equipped with thrusters along their body to improve maneuverability and provide hovering capabilities, and how this robot addresses current needs for subsea resident robots in the oil and gas industry, fish farming and other subsea applications.

Keywords: Time-varying systems, Linear systems, Observers for linear systems
Abstract: Uniform complete observability and controllability are key concepts for the design of state observers and controllers for linear time-varying systems. While the definitions of these properties are simple, it is more often that not very difficult to show they hold for a particular system. This paper presents alternative simpler sufficient conditions. Examples are shown that illustrate the improvement over previous results, the fact that the new conditions are not necessary, and a practical application.

Keywords: Time-invariant systems, Linear systems, Robust controller synthesis
Abstract: This paper presents a proof of the Large Gain Theorem using the Nyquist Stability Criterion. The minimum gain constraint stipulated by the Large Gain Theorem guarantees the open-loop transfer function encircles the point (-1,0) exactly P times in the counterclockwise direction, where P is the number of open-loop open right-half plane poles. This guarantees asymptotic stability of the feedback system, even in the presence of an unstable open-loop transfer function. The Nyquist interpretation of the Large Gain Theorem is compared to Nyquist interpretations of the Large Gain and Passivity Theorems. Applications of the Large Gain Theorem are discussed and numerical examples illustrating the concept of minimum gain and the Large Gain Theorem are presented.

Keywords: Time-invariant systems, Linear systems, Structural properties
Abstract: In this paper, the discretisation problem for sparse linear systems is revisited from a model approximation perspective. Instead of approximating the realisation matrices, our goal is to indeed focus on approximating the transfer function of the associated discretised system. Academic examples are included to validate the proposed theory.

Keywords: Linear multivariable systems, Decoupling problems, Systems with time-delays
Abstract: To extend inverted decoupling control method to the non-square multivariable time delay system, this paper presents a block inverted decoupling control approach with internal model structure. In this method, the non-square system is divided into two subsystems, and the decoupling structure is composed of two blocks. Each block in the designed structure is applied to each subsystem. With the proposed method, complex matrix computations are avoided, and the designed controller has low order. The stability of closed-loop system with the proposed method is analyzed in the paper. Numerical simulations are performed to illustrate the effectiveness of this methodology.

Keywords: Linear multivariable systems, Time-varying systems, Disturbance rejection (linear case)
Abstract: This paper is concerned with the Hankel operator of sampled-datasystems. The Hankel operator is usually defined as a mapping from the past input to the future output and its norm plays an important role in evaluating the performance of systems. Since the continuous-time mapping between the input and output is periodically time-varying (h-periodic, where h denotes the sampling period) in sampled-data systems, it matters when to take the time instant separating the past and the future when we define the Hankel operator for sampled-data systems. This paper takes an arbitrary Thetain[0,h) as such an instant, and considers the quasi L_infty/L_2 Hankel operator defined as the mapping from the past input in L_2(-infty,Theta) to the future output in L_infty[Theta,infty). The norm of this operator, which we call the quasi L_infty/L_2 Hankel norm at Theta, is then characterized in such a way that its numerical computation becomes possible. Then, regarding the computation of the L_infty/L_2 Hankel norm defined as the supremum of the quasi L_infty/L_2 Hankel norms over Thetain[0,h), some relationship is discussed between the arguments through such characterization and an alternative method developed in an earlier paper that is free from the computations of quasi L_infty/L_2 Hankel norms. A numerical example is studied to confirm the validity of the arguments in this paper.

Keywords: Linear systems, Lagrangian and Hamiltonian systems
Abstract: In this paper, a time-discretization framework is proposed for Port Controlled Hamiltonian (PCH) systems using combined discretization rules for the flows and efforts, which preserves the continuous-time structure. As examples for this formulation, three symplectic time-discretization schemes are presented, using classic discretization rules (implicit/explicit Euler, implicit mid-point and implicit trapezoidal), for the flows and efforts. As continuous-time model for exemplification using this framework, a linear capacitor microphone circuit is selected.

Keywords: Decoupling problems, Linear multivariable systems, Decentralized control
Abstract: The partitioning of a system model will condition the structure of the controller as well as its design. In order to partition a system model, one has to know what states and inputs to group together to define subsystem models. For a given partitioning, the total magnitude of the interactions between subsystem models is evaluated. Therefore, the partitioning problem seeking for weak interactions can be posed as a minimization problem. Initially, the problem is formulated as a non-linear integer minimization that is then relaxed into a linear integer programming problem. It is shown within this paper that cuts can be applied to the initial search space in order to find the least interacting partitioning; only composed of controllable subsystems. Two examples are given to demonstrate the methodology.

Keywords: Complex systems, Linear multivariable systems
Abstract: In this work, the mathemtical model of a railway passenger car with conventional and radial-elastic wheels is analyzed. Stochastic excitations are applied to the wheelsets. The local elasticity between the wheel and the rail is modelled by a linearized stiffness behaviour due to geometrical effects to simplify the controller design procedure for H_2 analysis. An optimization of the vertical dynamics is performed by reducing the H2 norms of the dynamic wheel forces while keeping the RMS gain of carbody accelerations at a constant level. It is seen that the controller performs well on the low frequency range where the ride comfort is more pronounced and partially on the high frequency range to suppress the environmental ground vibrations and structure-borne noise.

Keywords: Diagnosis, Probabilistic robustness, Randomized algorithms
Abstract: This paper studies the design of fault detection (FD) system for uncertain linear time-invariant (LTI) systems based on randomized algorithms. With the help of probabilistic robustness techniques, an iterative design approach is proposed to determine the post-filter of observer-based residual generators with direct incorporation of the probability distribution of the model parameter uncertainties into the design procedure. The effectiveness of the proposed approach is demonstrated with an illustrative example.

Keywords: Regulation (linear case), Tracking, Optimal control theory
Abstract: In this paper the problem of optimizing the output regulation of a weakly dual redundant plant is addressed. When the system is underactuated, only a subset of the outputs can be arbitrarily controlled, and the remaining ones are constrained. With a specific focus on quasiperiodic references, i.e. signals that can be written as trigonometric series, we investigate the problem of finding the input that minimizes the asymptotic output tracking error, and how such solution is related to the inputs associated to the singularly optimal regulation of each output.

Keywords: Linear multivariable systems, Decoupling problems
Abstract: This paper proposes a simple and low numerical complexity technique for the simultaneous achievement of open and closed loop diagonal dominance. The method is based on the introduction of a static high gain inner feedback which modifies the plant to attain a diagonal dominant open loop compensated plant over an entire bandwidth of frequencies. The technique can be also applied to uncertain plants. The sufficient conditions derived for the open loop diagonal dominance also assure the simultaneous achievement of closed loop diagonal dominance under weak assumptions on the diagonal elements of the decoupled controller robustly stabilizing the plant. These assumptions agree with the usual closed-loop performance specifications, so that closed-loop diagonal dominance is achieved without any extra complication on the controller design procedure. The main merits of the present method are: applicability to uncertain plants, ease of implementation and low computational cost. Two examples taken from the relevant literature, are given to show the effectiveness of the proposed approach.

Keywords: Linear systems, Time-invariant systems, Disturbance rejection (linear case)
Abstract: This paper studies the properties of the zeros of sampled-data systems. Since the basic study by Åström et al. that showed there can exist unstable zeros that have no relevance to the original continuous-time plant, there have been various studies that circumvent this effect by introducing various techniques. The present paper studies zeros of sampled-data systems from the standpoint of lifting. We will show the following: a) such unstable zeros are an artifact induced by the combination of the zero-order hold and the synchronous sampling; b) such zeros disappear when considered in the lifted plant; c) further, such an effect can be bypassed by a multirate technique. We will also review relationships with the existing techniques such as two-delay control. Analysis of some examples is given.

Keywords: Linear multivariable systems, Disturbance rejection (linear case), Observers for linear systems
Abstract: This paper develops a new design method for predictive repetitive control systems to track periodic reference signals or reject disturbances with bandlimited frequency content. In the presence of input disturbances acting on the system, the new design estimates these disturbances using an observer. This approach is complementary to the predictive repetitive control system designs previously reported where the periodic disturbance model was embedded in the controller. Supporting experimental results from application of the new design to a two joint robotic arm are given.

Keywords: Adaptive and robust control of automotive systems, Vehicle dynamic systems, Automotive system identification and modelling
Abstract: This paper presents autonomous landing of Unmanned Aerial Vehicles (UAVs) under unknown external disturbances and internal plant parameter uncertainties. The external disturbances such as wind shear, wind gust and ground effects are considered. The plant parameters uncertainties are also considered due to aerodynamic force and moment coefficients random perturbation. L1 adaptive controller with piece-wise-constant adaptation law is augmented over Nonlinear Dynamic Inversion (NDI) autopilot and implemented on Six Degree of Freedom (Six-DOF) model of a UAV. The NDI autopilot with two time scale separation is designed for the nominal plant. It tracks a reference trajectory computed from a path planning and guidance algorithm under undisturbed plant model with normal environment conditions. The L1 adaptive controller takes into account the disturbances and computes the adaptive control command which enables the plant trajectory tracking closer to the desired reference trajectory. The state predictor is designed to track the plant states smoothly by incorporating proportional and integral error terms in the state predictor model. The piece-wise-constant adaptive law is designed to estimate the unknown disturbances. The unknown disturbance estimates are used to design the adaptive control law to nullify the effect of the disturbance on the plant performance. The algorithm is simulated to show the auto landing performance on a Six-DOF UAV model.

Keywords: Engine modelling and control, Nonlinear and optimal automotive control, Simulation
Abstract: In this research, a second order sliding mode (SOSM) strategy to control the Atkinson cycle engine breathing subsystem with an alternative late intake valve closing (LIVC) scheme is presented. The control framework based on extended mean value engine model (EMVEM) of the Atkinson cycle engine in view of fuel economy is evaluated for the standard FUDS and US06 driving cycles. In this context, the authors have already proposed a control-oriented EMVEM model of the Atkinson cycle engine with variable intake valve actuation, well suited for SOSM design. To demonstrate the benefits of the VCR Atkinson cycle engine over conventional Otto cycle engine, in the presence of auxiliary loads and uncertain road loads, its EMVEM model is simulated by using a precisely tuned super-twisting controller having similar specifications as that of the conventional gasoline engine. The resulting approach confirms the significant decline in engine part load losses and improvement in the thermal efficiency and, accordingly, exhibits considerable enhancement in the fuel economy of the VCR Atkinson cycle engine over conventional Otto cycle engine during the wide operating range.

Keywords: Control architectures in automotive control, Autonomous Vehicles
Abstract: Stability control represents one of the most challenging tasks in autonomous vehicle dynamics. Within this context, model uncertainty may play a crucial role in determining the quality of the overall control system. In this work, we deal with nonlinear vehicle stability control design by using the D2-IBC (Data Driven - Inversion Based Control) method, wherein the dynamics of the system are modeled from data not to optimize the open-loop model matching but to maximize the closed-loop performance. Specifically, a multivariable extension of the method is derived and discussed in detail as far as the stability control application is concerned. This method will prove its effectiveness on a multi-body simulator of a 4-wheel steering autonomous vehicle.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Genetic algorithms in marine systems, Simulation
Abstract: This paper focuses on the development of mathematical models for vehicle frontal crashes. The models under consideration are threefold: a vehicle into barrier, vehicle-occupant and vehicle to vehicle frontal crashes. The first model is represented as a simple spring-mass-damper and the second case consists of a double-spring-mass-damper system, whereby the front mass and the rear mass represent the vehicle chassis and the occupant, respectively. The third model consists of a collision of two vehicles represented by two masses moving in opposite directions. The springs and dampers in the model are nonlinear piecewise functions of displacements and velocities respectively. More specifically, a genetic algorithm (GA) approach is proposed for estimating the parameters of vehicles front structure and restraint system for vehicle-occupant model. Finally, it is shown that the obtained models can accurately reproduce the real crash test data. Data for the vehicle into barrier were taken from the calibration test of a standard Ford Fiesta, model year 1987 at Grimstad in Norway. The second and third test data were taken from the National Highway Trafic Safety Administration (NHTSA).

Keywords: Vehicle dynamic systems, Trajectory Tracking and Path Following, Nonlinear and optimal automotive control
Abstract: The paper proposes a reconfigurable control design of steering and torque vectoring using a variable-geometry suspension system. Torque vectoring control is based on independent driving of the wheels. Simultaneously, the steering angle is generated by the variable-geometry suspension system by varying the camber angle of the front wheels. The efficiencies of the wheel tilting and the torque vectoring, and their coordinated actuation are in the focus of the paper. In the analysis a reachable set computation method on the polynomial model of the vehicle based on the trajectory reversing method and the Sum-of-Squares (SOS) programming are proposed. Based on the analysis results a reconfigurable control is designed using the Linear Parameter Varying (LPV) method.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Control architectures in automotive control, Control of systems in vehicles
Abstract: Vehicle Control Systems (VCS) for the lateral (steering) and longitudinal (velocity) dynamics are normally used to improve the handling properties of a vehicle. However, when different VCS operate simultaneously in the vehicle, the global performance could be degraded because of the cross-coupling dynamics. In this article the design of decentralized controllers for the steering and velocity subsystems is presented. The resulting control scheme reduces the cross/coupling among the steering and longitudinal dynamics. These results were achieved by proposing a more complete vehicle model and analyzing the cross-coupling using a multivariable analysis and design framework known as Individual Channel Analysis and Design (IChAD). These results can be extended for other coupled subsystems.

Keywords: Control architectures in automotive control, Nonlinear and optimal automotive control
Abstract: Drift parking represents an extreme maneuver that is beyond the skill set of the average driver, requiring adept use and timing of the handbrake, pedal brake, and steering wheel. The maneuver causes the vehicle to rotate rapidly and slide, nearly sideways (i.e. high side slip angle), into a desired parking spot. In this paper we propose a control strategy to quickly park a vehicle in a narrow space using a high side slip angle maneuver, or 'drift parking' maneuver. The proposed control scheme switches between nonlinear model predictive control and a linear feedforward-feedback policy. For experimental validation, we use an open source, low cost 1/10 scale RC vehicle called the Berkeley Autonomous Race Car developed at UC Berkeley.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Information processing and decision support, Neural networks
Abstract: Heavy-duty vehicle platooning has been an important research topic in recent years. By driving closely together, the vehicles save fuel by reducing total air drag and utilize the road more efficiently. Often the heavy-duty vehicles will catch-up in order to platoon while driving on the common stretch of road, and in this case, a good prediction of when the platoon merging will take place is required in order to make predictions about overall fuel savings and to automatically control the velocity prior to the merge. The vehicle speed prior to platoon merging is mostly influenced by the road grade and by the local traffic condition. In this paper, we examine the influence of road grade and propose a method for predicting platoon merge distance using vehicle speed prediction based on road grade. The proposed method is evaluated using experimental data from platoon merging test runs done on a highway with varying level of traffic. It is shown that under reasonable conditions the error in the merge distance prediction is smaller than 8%.

Keywords: Engine modelling and control
Abstract: For modern passenger car Diesel engines strong limitations on emissions, due to legislation, require large efforts in combustion and aftertreatment control. This control problem is mainly approached by an intensive stationary calibration process and dynamic compensation techniques. However, large differences in combustion conditions between steady state, as used for calibration, and real transient maneuvers, such as tip-ins, can lead to undesired emission peaks. Against this background, the present work adapts a previously introduced input shaping strategy for injection parameters, such that it is applicable in the full engine operation range. Different to the former approach, the shape of the correction profile is determined directly from identified local models of the selected injection parameters to the NOx response. By using local linear models it is possible to consider variations in the system dynamics and parameterize the input shapers in an automated fashion. Additionally this approach can be applied together with a state of the art engine control, since it does not need a change in the base calibration. The improved strategy is evaluated on a passenger car Diesel engine and it is shown that a reduction of the undesired NOx overshoot can be achieved without impacting other performance indicators.

Keywords: Vehicle dynamic systems, Control architectures in automotive control, Trajectory and Path Planning
Abstract: This paper deals with the active control with steering angle saturation constraint in vehicle to improve the safety and its stability. The reference tracking problem is addressed by minimizing both the lateral displacement and velocity errors. The vehicle dynamics is represented by a saturated input LPV (Linear Parameter Varying) system, affected by disturbances / uncertainties to be rejected. The corresponding state-space model considers both polytopic and norm-bounded uncertainties. It is aimed to compute a static output feedback gain, so as to place the set of all eigenvalues of the uncertain closed-loop state matrix mA_{C} in a disc, denoted by cD_D, while minimizing the effect of the disturbances on the system. This paper proposes an original technique, based on the resolution of lmi, to compute the gain matrix of the control law which can be structured as the parameters of a robust PID controller. In order to highlight the performance of the proposed control algorithm, numerical simulations are performed.

Keywords: Kalman filtering techniques in automotive control, Vehicle dynamic systems
Abstract: This paper is about the estimation of front suspension stroke in a motorcycle, oriented to the semi-active end-stop avoidance control. The proposed observer is based on a Kalman-filter that takes into account the external forces acting on suspension while the vehicle is turning or accelerating. Furthermore a practical state-reset strategy is proposed in order to improve estimation performances for wheelie manoeuvres and to limit the estimated state into suspension physical bounds. The experimental results show that position can be correctly estimated both on road and off-road driving conditions.

Keywords: Decision making and cognitive processes, Robotics technology, Intelligent controllers
Abstract: In this paper, a concept of {embodied intelligence} (EI) will be considered that is different from the `classical' definition. Though many ideas of the above two approaches to AI have recently been put into practice, unfortunately, none of the implementations can be fully identified with artificial intelligence. Projects that dare to approach AI and EI (like an autonomous car driver) should be based on both the AI concepts (symbolic and sub-symbolic), in solving real problems of ambient perception and decision-making. Therefore, Embodied (Artificial) Intelligence, in this paper, is understood as a methodology that uses all available resources and algorithms from the analytic and synthetic approaches, in order to implement an intelligent and autonomous agent. The xEmotion sub-system of ISD is designed to support the possibility of selecting a most appropriate response of an autonomous agent, or robot, to changes in the surrounding. Such agent under the control of the ISD system can perform efficiently due to a system of emotions, which - from the control theory viewpoint - can be interpreted as scheduling variables. The developed sub-system xEmotion integrated in a coherent way with psychological theories, allows the agent to create individual emotions, in response to observations, as it is in the case of an interactive avatar, dictobot, or an automatic autonomous xDriver, for instance. The processed emotions can be used both to promptly select a suitable reaction using the proposed two-level procedure, as well as to present emotions to another, external agent, which is in accordance with the contemporary trends in robotics.

Keywords: Automotive system identification and modelling, Engine modelling and control
Abstract: The contribution described in this paper is concentrated on the integratation of exhaust gas recirculation (EGR) system into the process of combustion in an optimal manner. In practice, deriving a state-space model of this actuator is an energetic task as a result of involving some uncertain chemical reactions. To alleviate the effect of unobserved phenomena, which does not seem to be easy in modeling, an improved Gaussian Process (GP) is represented for identifying such dynamics. In this approach, practical modification in general formulation of GP is provided based on proportional feedback gain adjustment. Afterwards, the obtained model is considered for design of optimal model-based control strategy. The whole aim is focused on achieving a green economically gasoline engine by optimizing the trend of fuel consumption. Eventually, simulation results illustrate the effectiveness of proposed structure in EGR systems.

Keywords: Stochastic system identification, Nonlinear system identification
Abstract: There are important information security problems for system identification, which have not been paid attention to and well studied. In this paper, we discuss the security problem of input information according to a class of system identification problems, and present security protocols in the sense of cryptography. It is ensured that the input information is not leaked during solving the identification problem, which can deal with passive attacks effectively. In addition, a quantitative relationship between the input information and the number of participants is given in this paper. The simulation results show that, in the range of allowed errors, the identification algorithm with the security protocol achieves the estimation accuracy. Moreover, the corresponding time complexity increases comparing with the original identification algorithm.

Keywords: Estimation and filtering, Continuous time system estimation, Filtering and smoothing
Abstract: This paper elaborates three well-known state estimators, which are used extensively in practice. These are the classical continuous-discrete extended Kalman filter (EKF) and the continuous-discrete cubature Kalman filtering (CKF) and unscented Kalman filtering (UKF) algorithms designed recently. Nowadays, it is commonly accepted that the contemporary filters always outperform the traditional EKF in the accuracy of state estimation because of their higher-order approximation of the mean of propagated Gaussian density in the time- and measurement-update steps of the modern techniques. However, the present paper specifies this commonly accepted opinion and shows that despite the mentioned theoretical fact the EKF may outperform the CKF and UKF methods in the accuracy of state estimation when the stochastic system under consideration exposes a stiff behavior. That is why stiff stochastic models are difficult to deal with and require effective state estimation algorithms to be devised yet.

Keywords: Estimation and filtering, Bayesian methods, Error quantification
Abstract: This article presents a Cramér-Rao lower bound for the discrete-time filtering problem under linear state constraints. A simple recursive algorithm is presented that extends the computation of the Cramér-Rao lower bound found in previous literature by one additional step in which the full-rank Fisher Information matrix is projected onto the tangent hyperplane of the constraint set. This makes it possible to compute the constrained Cramér-Rao lower bound for the discrete-time filtering problem without reparametrization of the original problem to remove redundancies in the state vector, which improves insights into the problem. It is shown that in case of a positive-definite Fisher Information Matrix the presented constrained Cramér-Rao bound is lower than the unconstrained Cramér-Rao bound. The bound is evaluated on an example.

Keywords: Estimation and filtering, Robust adaptive control
Abstract: Model Predictive Control (MPC) is the principal control technique used in industrial applications. Although it offers distinguishable qualities that make it ideal for industrial applications, it can be questioned its robustness regarding model uncertainties and external noises. In this paper we propose a robust MPC controller that merges the simplicity in the design of MPC with added robustness. In particular, our control system stems from the idea of adding robustness in the prediction phase of the algorithm through a specific robust Kalman filter recently introduced. Notably, the overall result is an algorithm very similar to classic MPC but that also provides the user with the possibility to tune the robustness of the control. To test the ability of the controller to deal with errors in modelling, we consider a servomechanism system characterized by nonlinear dynamics.

Keywords: Stochastic control and game theory, Stochastic hybrid systems, Optimal control of hybrid systems
Abstract: The incentive Stackelberg game for a class of Markov jump linear stochastic systems with one leader and multiple non-cooperative followers subjected to the H infinity constraint is investigated. An incentive structure is adopted that allows the leader's team-optimal solution with the H infinity constraint to be achieved. It is shown that the incentive strategy set can be obtained by solving the cross-coupled stochastic algebraic Riccati-type equations (CCSAREs). In order to demonstrate the effectiveness of the proposed scheme, a numerical example is solved.

Keywords: Estimation and filtering
Abstract: State estimation methods for lumped parameter nonlinear systems are presented in the paper. All the methods are based on a nonlinear predictor, which estimates future states through state evolution computations. The prediction may be made in several successive steps or in one step as limit of multistep computations. The nonlinear estimator is predictor – corrector type one, correction is made through optimization on a finite horizon, giving possibility for approximate minimum variance estimation for a class of nonlinear plants. At the same time, the presented predictor may replace the predictor of extended Kalman filter, making possible better prediction and overall estimation through modification of the extended Kalman filter algorithm.

Keywords: Estimation and filtering, Continuous time system estimation, Mechanical and aerospace estimation
Abstract: A large number of humanoid robot state estimators based on the Kalman filter (KF) have been proposed. However, such estimators cannot guarantee optimality when the system model is nonlinear or when non-Gaussian/correlated modelling error is present. Additionally, the use of a KF causes difficulty in coping with equality or inequality constraints. Because a bipedal humanoid robot is a complex nonlinear system, its mathematical model is limited in its ability to express the system accurately. Therefore, KF-based humanoid state estimation has unavoidable limitations. To overcome these, we propose a new approach to humanoid state estimation using a moving horizon estimation theory. So far, there are almost no studies on the moving horizon estimator-based humanoid state estimator, which is capable of accommodating nonlinear systems and constraints as well as being more robust to the non-Gaussian/correlated modelling error. The proposed estimator framework facilitates the use of a simple model, even in the presence of large modelling error. Additionally, it can estimate the humanoid state more accurately than the existing KF-based estimator framework. The performance and characteristics of the proposed approach were confirmed experimentally.

Keywords: Stochastic control and game theory, Optimal control theory, Control of switched systems
Abstract: In the present work, we study the mixed H2/Hoo state feedback control for Markov jump linear systems through a detector approach. It is considered that the Markov parameter θ(k) is not available to the controller, but instead there is an estimation ξ(k) given by a detector. Conditions for the synthesis of controllers that depend only on ξ(k) and stochastically stabilize the system while providing upper bounds for the H2 and Hoo norms are given in terms of linear matrix inequalities. The results are illustrated by a numerical example.

Keywords: Simulation of stochastic systems, Stochastic control and game theory
Abstract: This paper addresses an LMI formulation for stability analysis of Lur'e type Markov Jump Multiple-Input, Multiple-Output (MIMO) Systems. We consider two possibilities for the unknown feedback nonlinearity, either it is deterministic or it varies with the Markov jumps. Based on this information, we propose conditions for testing the system absolute stability. The theoretical results are illustrated by numerical examples.

Keywords: Estimation and filtering
Abstract: This paper presents a novel observer-based method for estimating the parameters of an exponentially damped sinusoid, including the frequency, amplitude, phase and the damping factor. A second-order sliding-mode-based adaptation law is designed for estimating the frequency and the damping factor, while amplitude and phase are obtainable from the said parameters by straightforward algebra. The stability and robustness analysis in presence of an additive disturbance proves the existence of a tuning parameter set for which the estimator's dynamics are asymptotic stable and the estimation error asymptotically converges to a residual set whose size depends on the amplitude of the disturbance. Numerical and comparative examples show the effectiveness of the proposed estimation approach.

Keywords: Estimation and filtering, Kalman filtering techniques in marine systems control, Autonomous underwater vehicles
Abstract: This work considers passive and active target tracking by the UUV (Unmanned underwater vehicle) on the basis of bearing-only observations including azimuth and elevation angles. The aim is realization of target motion analysis (TMA), that is simulteneous target's position and velocity estimates. Some of nonlinear filters can be used for such problem, however most of these filters produce estimations with unknown bias and quadratic errors. Moreover, as it was mentioned long ago possible unobservability could take place. In this work we use the pseudomeasurements Kalman filtering (PKF) method which transforms the estimation problem to the linear one and gives the possibility to evaluate the moving target coordinates and velocities without bias. Since PKF gives unbiased estimate for the motion and the quadratic error it provides the good means for intergration of varios measurements methods such as passive (bearing-only) and active (range) metering. Using this filtering approach the position and velocity target tracking has been developed. Computer simulation results demonstrate good quality of TMA for randomly moving target

Keywords: Synthesis of stochastic systems, Linear parametrically varying (LPV) methodologies, Stochastic control and game theory
Abstract: Linear discrete-time systems with stochastic and deterministic polytopic type uncertainties in their state-space model are considered. A dynamic output-feedback controller is obtained via a new approach which allows a derivation of a controller in spite of parameter uncertainty. In the proposed approach the system is described via a difference equation and an augmented system is then used to obtain the output-feedback controller parameters. The controller is obtained without assuming a specific structure to the quadratic Lyapunov function and it is the first time that an output-feedback controller is obtained for robust state-multiplicative systems. The controller minimizes the stochastic L_2-gain of the closed-loop where a cost function is defined to be the expected value of the standard H_infty performance index with respect to the stochastic uncertainty. An example is given that demonstrates the merit of the theory.

Keywords: Estimation and filtering, Stochastic control and game theory, Control over networks
Abstract: This paper considers the remote state estimation in a cyber-physical system (CPS) using multiple sensors. The measurements of each sensor are transmitted to a remote estimator over a shared channel, where simultaneous transmissions from other sensors are regarded as interference signals. In such a competitive environment, each sensor needs to choose its transmission power for sending data packets taking into account of other sensors' behavior. To model this interactive decision-making process among the sensors, we introduce a multi-player non-cooperative game framework. To overcome the inefficiency arising from the Nash equilibrium (NE) solution, we propose a correlation policy, along with the notion of correlation equilibrium (CE). An analytical comparison of the game value between the NE and the CE is provided, with/without the power expenditure constraints for each sensor. Also, numerical simulations demonstrate the comparison results.

Keywords: Estimation and filtering, Sensor networks, Control and estimation with data loss
Abstract: This paper studies the fusion estimation problem for a class of networked multi-sensor fusion systems (NMFSs) under bandwidth constraints and denial-of-service (DoS) attacks. The dimensionality reduction strategy is used to satisfy the finite bandwidth, while the prediction compensation strategy is used to reduce the information loss caused by attacks. Based on dimensionality reduction and DoS attack model, a recursive distributed Kalman fusion estimator (DKFE) is designed for the addressed NMFSs. An explicit form of the dimensionality reduction is given against DoS attacks, while an effective attack strategy is proposed for the attacker. An illustrative example is given to show the effectiveness of the proposed methods.

Keywords: Stochastic control and game theory
Abstract: In this paper, we deal with discrete-time linear stochastic systems whose state transition is described by a sequence of random matrices determined by a Markov process. For such systems, we first show a linear-matrix-inequality-based stability condition. The use of conditional expectation plays a key role in the derivation. Then, we assume that the systems are uncertain in the sense that they are described by a sequence of random polytopes determined by a Markov process (to which the actual underlying random matrix sequence should belong), and extend the result about the above uncertainty-free case toward robust stability analysis, which leads to our main result. To show the usefulness of the result, we apply it to the case with stochastic switched systems having uncertain coefficient matrices, and provide a numerical example.

Keywords: Stochastic control and game theory
Abstract: This paper considers the linear quadratic Gaussian (LQG) control problem for discrete-time systems subject to multiplicative noises and input delay. When the state variables can be obtained exactly, the necessary and sufficient condition for the existence of a unique solution to the LQG control has been presented and an explicit analytical expression is given for the optimal LQG controller. More specially, the optimal LQG controller is derived which consists of the feedback form of the conditional expectation of the state and one additive deterministic term based on the coupled Riccati equations. Through the completing square approach, the optimal controller is obtained based on the solution to the forward and backward stochastic difference equations (FBSDEs).

Keywords: Estimation and filtering, Randomized methods, Simulation of stochastic systems
Abstract: This paper is motivated by the problem of more accurately estimating the hidden state in nonlinear dynamic system. We propose a uniform random sampling Kalman filter(URSKF) which can be regarded as a Sigma-point filter based on the statistical linearization method. Compared with other Sigma-point filters, the uniform random sampling method used to obtain the deterministic points can match the any-order moment of the prior distribution with the moderately increasing sampling points and automatically capture the more rich statistical properties of the system after nonlinear function mapping. Moreover, the URSKF is a derivative and square-rooting free operation which avoid computing Jacobian matrix and failing the filtering process for not always guaranteeing the covariance matrix to be positive definite, such as UKF. Besides, the computation complexity is linearly related to the system dimension avoiding the curse of high dimension in the Gauss-Hermite Quadratute Filter(GHQF). The performance of this filter is demonstrated by an aircraft tracking model. The simulation results show the URSKF performs higher accurately than the Unscented Kalman Filter(UKF), Central Difference Kalman Filter(CDKF) and Cubature Kalman Filter(CKF).

Keywords: Estimation and filtering, Error quantification, Continuous time system estimation
Abstract: The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated by the UKF are used for covariance estimation by MLE and CM. Then we apply the two covariance estimation methods on an example application. In the example, we identify the covariance of the measurement noise for a continuous glucose monitoring (CGM) sensor. The sensor measures the subcutaneous glucose concentration for a type 1 diabetes patient. The root-mean square (RMS) error and the computation time are used to compare the performance of the two covariance estimation methods. The results indicate that as the prediction horizon expands, the RMS error for the MLE declines, while the error remains relatively large for the CM method. For larger prediction horizons, the MLE provides an estimate of the noise covariance that is less biased than the estimate by the CM method. The CM method is computationally less expensive though.

Keywords: Estimation and filtering, Grey box modelling, Particle filtering/Monte Carlo methods
Abstract: A turbocharger is a fundamental device to improve power of internal combustion engines. In this paper, we focus on plants constituted by two turbochargers, each of which composed by the centrifugal compressor and the turbine, working together. In order to ensure the reliable and efficient operation of the turbochargers, new maintenance strategies are being developed, often based on the continuous measurement and monitoring of key variables. To this purpose, a model, which can be constructed from first principles, is a fundamental tool. However, some main variables are not accessible, posing an estimation problem. Herein, an innovative approach based on particle filtering is proposed. Its validity is assessed through experimental results reported in the paper.

Keywords: Stochastic control and game theory, Optimal control of hybrid systems, Stochastic hybrid systems
Abstract: We investigate some stability properties of approximate null-controllability (A0C). We show that A0C is inherited when increasing the number of observed jumps. However, in all generality, there is no direct connection between A0C with an initial mode and the A0C starting from directly accessible modes. We have also given a sufficient condition under which checking A0C implies AC and discuss some examples. Unfortunately, such conditions are too strong and do not cover all AC systems (see examples). In a second part, we give a Riccati technique-based viability kernel characterisation of A0C. Some conditions are inferred for AC.

Keywords: Stochastic control and game theory, Adaptive system and control, Bayesian methods
Abstract: Computed-torque control requires a very precise dynamical model of the robot for compensating the manipulator dynamics. This allows reduction of the controller's feedback gains resulting in disturbance attenuation and other advantages. Finding precise models for manipulators is often difficult with parametric approaches, e.g. in the presence of complex friction or flexible links. Therefore, we propose a novel computed-torque control law which consists of a PD feedback and a dynamic feed forward compensation part with Gaussian Processes. For this purpose, the nonparametric Gaussian Process regression infers the difference between an estimated and the true dynamics. In contrast to other approaches, we can guarantee that the tracking error is stochastically bounded. Furthermore, if the number of training points tends to infinity, the tracking error is asymptotically stable in the large. In simulation and with an experiment, we demonstrate the applicability of the proposed control law and that it outperforms classical computed-torque approaches in terms of tracking precision.

Keywords: Estimation and filtering, Continuous time system estimation, Synthesis of stochastic systems
Abstract: This paper is about the H-infinity observer design for a class of nonlinear singular stochastic systems. The system is first transformed such that the observation error can be written in a regular form and the augmented system is admissible. The use of Sylvester-like equations on the error dynamics permits to parameterize all the filter matrices via a single gain matrix. Then an affine change of coordinate is done on the control inputs to transform the problem into a singular robust stochastic nonlinear problem which is solved using a Lyapunov approach. Finally the mean square exponential stability (MSES) of the error's system and the H-infinity disturbance attenuation are ensured by the observer designed via the resolution of a LMI which permits to get the gain matrix used to construct the filter matrices.

Keywords: Estimation and filtering, Bayesian methods, Sensor networks
Abstract: In this paper we consider the problem of detecting which Markov chain model generates observed time series data. We consider two Markov chains. The state of the Markov chain cannot be observed directly, only a function of the state can be observed. Using these observations, the aim is to find which of the two Markov chains has generated the observations. We consider two observers. Each observer observes a function of the state of the Markov chains. We formulate a binary hypothesis testing problem for each observer. Each observer makes a decision on the hypothesis based on its observations. Then observer 1 communicates its decision to observer 2 and vice-versa. If the decisions are the same, then a consensus has been achieved. If their decisions are different then the binary hypothesis testing problem is continued. This process is repeated until consensus has been achieved. We solve the binary hypothesis testing problem and prove the convergence of the consensus algorithm. The “value” of the information gained through 1-bit communication is discussed along with simulation results.

Keywords: Learning theory, Statistical data analysis, Nonlinear system identification
Abstract: We present a new modification of the gradient descent algorithm based on the surrogate optimization with projection into low-dimensional space. It iteratively approximates the target function in low-dimensional space and takes the approximation optimum point mapped back to original parameter space as next parameter estimate. Main contribution of the proposed method is in application of projection idea in approximation process. Major advantage of the proposed modification is that it does not change the gradient descent iterations, thus it can be used with some other variants of the gradient descent. We give a theoretical motivation for the proposed algorithm and a theoretical lower bound for its accuracy. Finally, we experimentally study its properties on modelled data.

Keywords: Statistical data analysis, Time series modelling, Continuous time system estimation
Abstract: The patient falls out of bed in hospital have caused a serious problem that reduces the quality of life as well as increases the health-care costs. The aim of this study is to investigate the low cost data-driven bed-exit detection method for an intelligent nursing bed to provide alarms in advance that the patients are about to exit the bed only using real-time monitoring signals collected from four load cells installed at corners of bed. First, the coordinate equations of the position center are deduced according to the moment balance theory. Second, some typical patterns of patient’s actions on the bed are identified and distinguished using Fisher discriminant analysis (FDA) when the patient is found outside a predetermined region. Third, an alarm rule is defined by combing the calculation of the gravity center and the recognition of patient’s action pattern. The developed bed-exit method is verified based on experimental data of 102 hours collected from 14 adult volunteers participated in this study. Illustration results show that the proposed method can detect bed-exit event 21.00 seconds ahead of time with reliable sensitivity values of 100% and ratio of false alarms of 7.47%.

Keywords: Statistical data analysis, Simulation of stochastic systems
Abstract: Simulation is a widely used discipline to analyze and evaluate the outputs of a defined process in order to improve it. However, in domains such as healthcare, management rules or distribution functions are not techniques relevant enough to route the entities in the models. In this paper we propose KITE, a methodology to improve the extraction of knowledge from process-oriented datasets. We apply this methodology to the extraction of routing rules for healthcare simulation models.

Keywords: Statistical data analysis, Fault detection and diagnosis
Abstract: We present a new chamber matching algorithm designed for the semiconductor industry, which is completely data-driven and unsupervised. The behavior of an equipment is classified as different when the shape of the time series given by one of the sensors is significantly different. Shape comparison is performed using linear regression, hence allowing both offset and change of scale.

The method detects both the chamber and the sensor in which the fault is present, then helping in activating corrective maintenances. Application results are shown with two examples of real failures in the semiconductor industry.

Keywords: Wastewater treatment processes, Dynamics and control, Monitoring
Abstract: Turbidity is an important process variable on water treatment processes for safeguard health requirements, and dissolved air flotation (DAF) is a physical treatment that allows water turbidity reduction by floating particles dispersed on water. Hence, a DAF pilot plant was assembled and automated using supervisory control and data acquisition (SCADA), open platform of communication protocol (OPC) and Ethernet to test two possible strategies for water turbidity control. The results indicated that manipulating needle valve opening has advantages on DAF control.

Keywords: Parameter and state estimation, Wastewater treatment processes, Modeling and identification
Abstract: Aeration of biological reactors in wastewater treatment plants is important to obtain a high removal of soluble organic matter as well as for nitrification but requires a significant use of energy. It is hence of importance to control the aeration rate, for example, by ammonium feedback control. The goal of this paper is to model the dynamics from the set point of an existing dissolved oxygen controller to effluent ammonia using two types of system identification methods for a Hammerstein model, including a newly developed recursive variant. The models are estimated and evaluated using noise corrupted data from a complex mechanistic model (Activated Sludge Model no.1). The performance of the estimated nonlinear models are compared with an estimated linear model and it is shown that the nonlinear models give a significantly better fit to the data. The resulting models may be used for adaptive control (using the recursive Hammerstein variant), gain-scheduling control, L2 stability analysis, and model based fault detection.

Keywords: Dynamics and control, Modeling and identification, Wastewater treatment processes
Abstract: The operation of an anaerobic digestion located on a farm and primarily fed by cattle slurry, manure and ensiled maize, has been simulated using the Anaerobic Digestion Model 1, combined with a thermodynamic model. The effect of temperature on the biological reactions has been taken into account, as well as the limited availability of the substrates. This can be compensated by co-digesting other substrates easily found in the vicinity of the farm such as glycerol (waste from biodiesel production). Strategies based on glycerol feeding or temperature set points change have been investigated in order to control the methane production.

Keywords: Modeling and identification of environmental systems, Modeling and identification, Wastewater treatment processes
Abstract: The identification of kinetic models can be simplified via the computation of extents of reaction on the basis of invariants such as stoichiometric balances. In the extent space, one can identify the structure and the parameters of reaction rates individually, which significantly reduces the number of parameters that need to be estimated simultaneously. So far, extent-based modeling has only been applied to cases where all the extents can be computed from measured concentrations. This generally excludes its application to many biological processes for which the number of reactions tends to be larger than the number of measured quantities. This paper shows that, in some cases, such restrictions can be lifted. In addition, this study demonstrates the applicability of extent-based model identification using laboratory experimental data.

Keywords: Wastewater treatment processes, Modeling and identification
Abstract: Sulfur is known to cause nuisances in wastewater treatment and sewer collection systems, such as corrosion, odours, oxygen demand, and precipitates. This is often handled through chemical addition, which will impact the subsequent wastewater treatment processes. Despite its importance, there is no integrated sulfur model for whole plant process optimization/design that could be used in practice. After a detailed literature review of chemical and biokinetic sulfur reactions, a plantwide model is upgraded with relevant reactions to predict the sulfur cycle in sewer collection systems, wastewater and sludge treatment as well as to estimate a risk of odour emissions. The developed model is applied on different case studies.

Keywords: Modeling and identification of environmental systems, Water quality and quantity management
Abstract: Urban drainage networks (UDN) carry urban wastewater to wastewater treatment plants (WWTP) in order to regenerate it before releasing it to the environment. Combined UDN (CUDN) carries both rain and wastewater together, which can overload the UDN and produce combined sewer overflows (CSO) that irreversibly pollute the environment. Management of CUDN is receiving increasing attention from both researchers and water managers, in order to meet the high quality standards required for water and environment according to Water Framework Directive. Because of the complex dynamics of water quality, integrated control of CUDN considering both flows and quality of the conveyed wastewater is a difficult problem. In order to operate CUDN efficiently, real-time control (RTC) based on a conceptual quality model appears as a suitable option. This paper mainly presents a simplified conceptual quality modelling approach to represent the dynamics of suspended solid in sewers of CUDN oriented to real-time control. A simulator of SWMM (storm water management model) integrated with a lumped conceptual model for total suspended solid (TSS) is used for calibration and validation. A real example of Perinot sewer network is used as a case study. Discussions about RTC implementation in CUDN are also provided in this paper, where Model Predictive Control (MPC) is proposed as the optimal method to control the integrated water and quality models in CUDN as future motivation.

Keywords: Robust linear matrix inequalities, Robust control, Linear multivariable systems
Abstract: This work presents an application of Linear Matrix Inequalities (LMI) for the robust control of an F-16 aircraft through an algorithm that ensuring the damping factor to the closed-loop system. The results of some tests show that the zero and gain settings are sufficient to ensure robust performance and stability with respect to various operation points. This is one of the main characteristics of LMI’s, which are mathematical tools widely applied in control theory, which allow the simultaneous treatment of several performance and robustness requirements. The pole placement is the technique used with the aim to put the system poles in closed-loop in a specific region of the complex plane. Test results using a lateral dynamic model of the F-16 aircraft are presented and discussed.

Keywords: Robust controller synthesis, Robust control (linear case), Controller constraints and structure
Abstract: Given a Linear Time Invariant (LTI) system with parametric uncertainties, we propose a new method to synthesize a structured controller with H_infty constraints. Our approach is based on global optimization. The problem is formulated as a min-max optimization problem. A new version of a global optimization algorithm based on interval arithmetic is implemented to solve this kind of problems. To validate our approach, an example of Autonomous Underwater Vehicle (AUV) regulation to synthesize a PID controller with two H_infty constraints and two parametric uncertainties is given.

Keywords: Robust linear matrix inequalities, Lyapunov methods, Aerospace
Abstract: This paper provides a semidefinite optimization solution to the analysis and control of a rigid rotating spacecraft. Due to the quaternion representation of a rotating body, the nonlinearities that describe the system are polynomial in nature and may be expressed in the Dierential Algebraic Representation (DAR). Once the system is in this form, it is possible to apply linear-based tools for the analysis and control design of the closed loop dynamics. The task is then reduced to that of a semidenite optimization problem subject to constraints in the form of Linear Matrix Inequalities (LMIs).

Keywords: Robustness analysis, Uncertainty descriptions
Abstract: The landing of a civil transport aircraft is one of the most challenging phases. The reason mainly lies in the need to satisfy different concurrent requirements in the face of a wide range of system variations and environmental perturbations. Automatic control laws have been developed in the past to address this complex scenario, using an involved iterative process of design, tuning and validation. Due to the uncertainties and perturbations present in this phase, robust synthesis techniques (e.g standard and structured H_inf design) provide an effective framework to accomplish these tasks. This compels on the other hand to rely on procedures to generate the proper mathematical description of the plant and the availability of analysis tools to obtain time efficient robust predictions and insight into the system being analyzed. This work presents the application of Linear Fractional Transformation modeling and mu analysis to the Aircraft Landing Benchmark proposed by ONERA and Airbus.

Keywords: Robust controller synthesis, Controller constraints and structure, Robust control applications
Abstract: This paper addresses the flare design of a civilian aircraft within the benchmark proposed by ONERA and AIRBUS to enhance the robustness and capabilities of autoland control systems. The flare segment must perform soft landings within a desired range of touchdown points on the runway in the presence of parametric uncertainties, strong wind turbulence and ground effects. In this paper, a structured H-infinity approach based on the standard H-infinity formulation is explored for the vertical speed controller. In addition, some strategies to improve the robustness of the autoland control system against some critical parameters are described. Finally, the longitudinal performance and robustness of the control system are examined through nonlinear nominal simulations and Monte-Carlo analysis.

Keywords: Robust control applications, Aerospace
Abstract: A comprehensive autolanding design for the representative model of a twin-engined commercial aircraft is presented in this paper. A cascaded structure is selected to facilitate the design task and to minimize control law switching. Classical loopshaping and robust control techniques are used to design the individual control loops. The emphasis is on design parameters with a clear relation to design objectives, resulting in an intuitive tuning procedure. The control system's ability to safely land the aircraft despite strong crosswind in a variety of possible scenarios is demonstrated in an industry-grade verification campaign. Monte Carlo simulations are used to assess the risk of unsafe landing conditions and provide insight into the performance characteristics and limitations of the proposed control system.

Keywords: Closed loop identification
Abstract: In this paper we focus on consistently identifying a transfer function (module) embedded in a dynamic network. When identifying a module embedded in a dynamic network, a critical choice is which variables to include as predictor inputs. In the system identification literature sufficient conditions have been derived. One condition is that there should be no confounding variables. We show that this condition can be relaxed.

Keywords: Closed loop identification, Bayesian methods
Abstract: There are recently studies on linear system identification with high order finite impulse response (FIR) models using the regularized least-squares approach. The regularized approach allows some bias to reduce variance so that the error of the regularized FIR model is minimized. This paper concerns the comparison between the regularized least-square approach and the traditional predictive error method (PEM), using a number of case studies. More cases are included than those used by the authors of the regularized approach, for example, non-white input signal, non-white output noise, low-order systems and closed-loop tests. In most cases PEM outperforms the regularized approach.

Keywords: Closed loop identification, Identification for control
Abstract: Identification based PID tuning is studied in this work. A method is proposed to achieve the informativity of the closed-loop system. The basic idea is shifting the PID parameters appropriately according to the amplitude of the feedback error. In this way, not only the closed-loop system can be identified accurately without using test signals, but also the control performance can be improved during the data collection period. An internal model control (IMC) design method is applied to tune the PID parameters. The effectiveness of the proposed approach is illustrated by simulations.

Keywords: Nonlinear system identification, Adaptive system and control, Closed loop identification
Abstract: We propose a method for deriving computationally efficient representations of periodic solutions of parameterized systems of nonlinear ordinary differential equations. These representations depend on parameters of the system explicitly, as quadratures of parameterized computable functions. The method applies to systems featuring both linear and nonlinear parameterization, and time-varying right-hand-side; it opens possibilities to invoke scalable parallel computations for numerical evaluation of solutions for various parameter values. Application of the method to parameter estimation problems is illustrated with constructing an algorithm for state and parameter estimation for the Morris-Lecar system.

Keywords: Recursive identification, Continuous time system estimation, Closed loop identification
Abstract: Model estimation of industrial processes is often done in closed loop due, for instance, to production constraints or safety reasons. On the other hand, many processes are time-varying because of aging effects or changes in the environmental conditions. In this study, a recursive estimation algorithm for linear, continuous-time, slowly time-varying systems operating in closed loop, is developed. The proposed method consists in coupling linear filter approaches to handle the time-derivative, with closed-loop instrumental variable (IV) techniques to deal with measurement noise. Simulations show the advantages of using this IV-based method.

Keywords: Bounded error identification
Abstract: The general problem addressed in this paper is that of the estimation and prediction of the aircraft flight maneuvering envelope. This work is intended to be further used as the basis of a new approach for predictive safe flight envelope monitoring and early notification of developing off-nominal flight situations. The proposed method combines: i) a Zonotopic Kalman Filter (ZKF) to estimate the set of possible states related to the open-loop flight dynamics of an aircraft, ii) reachable tube computations based on a closed-loop model to predict the future evolution of an aircraft over a specified time window and based on some assumptions about future control activities. The reachable tubes over the moving anticipatory temporal window are initialized and computed at each sample time based on the estimated state set resulting from the ZKF prediction/correction observation scheme which takes into account bounded state and measurement uncertainties. The aircraft benchmark CALC (Civilian Aircraft Landing Challenge) jointly proposed by ONERA (French Aerospace lab) and AIRBUS has been used to provide some preliminary simulation results.

Keywords: Bounded error identification, Cooperative systems, Fault detection and diagnosis
Abstract: In this paper, the problem of joint state and unknown input estimation for linear time-invariant (LTI) discrete-time systems using interval observer is addressed. This problem has already been studied in the context of continuous-time systems. To the best of our knowledge, unknown input interval-based estimation for discrete-time systems has not been considered in the litterature. Assuming that the measurement noise and disturbances are bounded, lower and upper bounds are first computed for the unmeasured state and then for the unknown inputs. The results obtained with a numerical example highlight the efficiency of the method.

Keywords: Bounded error identification, Event-based control
Abstract: This paper deals with the problem of state estimation for linear continuous-time systems based on the Prediction-Correction (PC) set-membership approach. Although the abundant literature on using the PC estimation approach, the case of event-based measurement sampling is still poorly investigated. In the developed approach, the measurement in the correction step of the PC approach is taken only when a prior defined threshold is crossed. A convergence analysis is proposed for ensuring the boundedness of the estimated state enclosure. The effectiveness of the proposed approach is shown through numerical simulation for an unobservable linear system. This approach is compared to the classical PC approach where the measurement is continuously taken.

Keywords: Bounded error identification, Linear parametrically varying (LPV) methodologies, Continuous time system estimation
Abstract: This paper presents an actuator fault compensation approach for a class of Linear Parameter-Varying (LPV) systems with noisy measurements. The proposed method is based on interval estimation assuming that the fault vector and the external disturbances are unknown but bounded. The main idea consists in designing a control law, based on a linear state feedback, to guarantee closed-loop stability. An additive control, based on fault bounds, is used to compensate the impact of actuator faults on system performances. The closed-loop stability of the robust fault compensation scheme is established in the Lyapunov sense. Finally, the theoretical results are illustrated using a numerical example.

Keywords: Distributed control and estimation
Abstract: A distributed set-membership approach is proposed for the state estimation of large-scale systems. The uncertain system states are bounded in a sequence of the distributed set-membership estimators considering unknown-but-bounded system disturbances and measurement noise. In the framework of the set-membership approach, the measurement consistency test is implemented by finding parameterized intersection zonotopes. The size of the intersection zonotope is minimized by solving an optimization problem including a sequence of linear/bilinear matrix inequalities based on the weighted 2-norm criterion of the generator matrix. Meanwhile, for the distributed set-membership estimators, the partial projection method is considered to correct the estimation of the neighbor state. On the other hand, an on-line method is also provided. Finally, the proposed distributed set-membership approach is verifed in a case study based on a urban drainage network.

Keywords: Distributed control and estimation, Continuous time system estimation
Abstract: The problem of state estimation for heat non-homogeneous equations under distributed in space measurements is considered. An interval observer is designed, described by Partial Differential Equations (PDEs), for uncertain distributed parameter systems without application of finite-element approximations. Conditions of boundedness of solutions of interval observer with non-zero boundary conditions and measurement noise are proposed. The results are illustrated by numerical experiments with an academic example.

Keywords: Optimal control theory
Abstract: This paper deals with the problem of rational stabilizability of nonlinear control systems by optimal control. Many sufficient conditions are derived characterizing the partial rational stabilizability by optimal feedback laws. Our main results are applied on the class of control systems with drift, where an optimal feedback laws are built stabilizing partially these systems with the rate decay displaystyle frac{1}{t^{alpha}},,alpha>0.

Keywords: Optimal control theory, Hamiltonian trajectories in optimal control, Modeling for control optimization
Abstract: In modeling the dynamics of capital, the Ramsey equation coupled with the Cobb-Douglas production function is reduced to a linear differential equation by means of the Bernoulli substitution. This equation is used in the optimal growth problem with logarithmic preferences. We consider a vector field of the Hamiltonian system in the Pontryagin maximum principle, taking into account the control constraints. We prove the existence of two alternative steady states, depending on the constraints. A proposed algorithm for constructing growth trajectories combines methods of open-loop control and closed-loop stabilizing control. Results are supported by modeling examples.

Keywords: Optimal control theory, Control of constrained systems, Real-time control
Abstract: This paper presents a Successive Convexification (SCvx) algorithm to solve a class of non-convex optimal control problems with certain types of state constraints. Sources of non-convexity may include nonlinear dynamics and non-convex state/control constraints. To tackle the challenge posed by non-convexity, first we utilize exact penalty function to handle the nonlinear dynamics. Then the proposed algorithm successively convexifies the problem via a project-and-linearize procedure. Thus a finite dimensional convex programming subproblem is solved at each succession, which can be done efficiently with fast Interior Point Method (IPM) solvers. Global convergence to a local optimum is demonstrated with certain convexity assumptions, which are satisfied in a broad range of optimal control problems. The proposed algorithm is particularly suitable for solving trajectory planning problems with collision avoidance constraints. Through numerical simulations, we demonstrate that the algorithm converges reliably after only a few successions. Thus with powerful IPM based custom solvers, the algorithm can be implemented onboard for real-time autonomous control applications.

Keywords: Optimal control theory, Control of constrained systems, Systems with saturation
Abstract: The inequality constrained nonlinear optimal control problem is difficult to handle using indirect numerical methods, since solving the resulting two-point boundary value problem (TPBVP) usually requires a priori information about the structure of the solution. In this paper, a fast and accurate indirect method is proposed for a class of inequality constrained optimal control problems. Firstly, the analytic form of the optimal control is proved for the problem subject to input saturations. Secondly, trigonometric regularization function is designed and incorporated in the cost function to implement the state constraints. Then the state and input constrained optimal control problem is directly transformed into a standard two-point boundary value problem without constraints. Comparisons with the pseudospectral method are conducted to verify the effectiveness and efficiency of the proposed method. In the input constrained case, the proposed indirect method gives more accurate solution with much faster convergence speed. While for the optimal control problem with both input and state constraints, the proposed method sacrifices a little accuracy for a faster speed.

Keywords: Optimal control theory, Control of constrained systems, UAVs
Abstract: In recent years, Unmanned Aerial Vehicles (UAVs) have become feasible solutions to various applications such as military flight missions, search and rescue operations, and geological mapping. The attitude control systems for UAVs need to be very robust to ensure safe and stable flights. To improve the stability beyond conventional control methods, a novel Model Predictive Control (MPC) formulation is proposed. In this paper, the mathematical derivation of the MPC controller is discussed. The novelty lies in the three-termed MPC cost function for attitude flight stability. The control strategy introduced in this paper is validated with experiments on a hexacopter in a custom fabricated experimental rig and with outdoor flight tests. The results show that the UAV is able to follow the operator's aggressive manoeuvring commands. Based on the data obtained, the MPC controller appears to be a promising control method to improve UAVs' performance.

Keywords: Optimal control theory, Control problems under conflict and/or uncertainties, Application of nonlinear analysis and design
Abstract: We consider a reachability problem for a nonlinear affine-control system with quadratic integral constraints. For pointwise constrained control inputs it is known that a control function steering the trajectory to the boundary of its reachable set satisfies the Pontryagin maximum principle. For the case of integral constraints we show that every admissible control that steers the control system to the boundary of reachable set provides a local solution to some optimal control problem with integral cost functional. The last implies the Pontryagin maximum principle for the extremal controls. A numerical procedure for calculating the reachable sets based on the Pontryagin maximum principle is proposed.

Keywords: Disturbance rejection, Robust control, Application of nonlinear analysis and design
Abstract: In this paper, we propose a finite-time disturbance observer-based robust control for output tracking of the Inteco three-tank system in the presence of mismatched nonlinear uncertainties. The controller is designed in a backstepping manner. At each step of the virtual controller design, a robust feedback controller with some effective nonlinear damping terms is designed so that the system states remain in the feasible domain. The virtual controller is enhanced by a finite-time disturbance observer, and the dynamic surface control technique which employs a first-order low-pass filter is adopted at each step of the virtual controller design to avoid the shortcoming of “explosion of terms”. Theoretical analysis is performed to clarify the control performance and simulation studies on the Inteco three-tank system model are included to show the efficacy of the proposed control method.

Keywords: Application of nonlinear analysis and design, Control of constrained systems, Real-time control
Abstract: This paper presents the control design problem for the real-time swing-up and stabilization of a double pendulum on a cart from a large set of non-stationary initial conditions. The proposed algorithm generates a tree of feedback stabilized trajectories leading to the upper equilibrium of the pendulum. For each trajectory in the tree, the algorithm determines the set of states near the nominal trajectory from which the system can be transfered to the upper equilibrium without violating the state and input constraints using the feedback controller of the trajectory. This set of states is defined as the funnel of the stabilized trajectory. The design of a control strategy for a large set of initial states is equivalent to the coverage of the desired region in state space using funnels. Several modifications of the original LQR-trees algorithm are introduced in this contribution, which includes a discretization based approximation of the funnel, the exploration of symmetric property of the double pendulum and the coverage of a design set which has a lower dimension than the state space. We show both simulation results and experimental validation of the proposed method.

Keywords: Application of nonlinear analysis and design, Optimal control theory, Linear parameter-varying systems
Abstract: A novel form of Nonlinear Generalized Minimum Variance controller is introduced, which has properties somewhere between those of a traditional model predictive controller and a Generalized Minimum Variance controller. Future set point information can be taken into account and an end state weighting can be introduced, but the algorithm is simpler than a traditional model predictive controller. The system includes a quasi linear parameter varying model and an input subsystem represented by a general nonlinear operator.

Keywords: Application of nonlinear analysis and design, Sum-of-squares, Convex optimization
Abstract: This paper concerns the simulation of a class of nonlinear discrete-time systems under a set of initial conditions described by an ellipsoid. We derive a procedure allowing the propagation of such ellipsoids through time, which makes it possible to set a guaranteed hard bound on the evolution of the state of the system for all the possible initial conditions. At the end of the paper, we show an application of the method through three academics examples, two of which are taken from the theory of fractals.

Keywords: Application of nonlinear analysis and design, Time-varying systems, Adaptive control
Abstract: We present a simple time-varying controller for tracking problem of mobile robots. We consider the full model of autonomous vehicles, including both the kinematics and the Lagrangian dynamics equations. Our control approach relies on designing a controller at the kinematics level, under any integrable virtual leader velocities, which is robust to any controller, at the torc level, that guarantees that the velocity errors are square integrable. In addition, we assume that the inertia is unknown hence, we use a passivity-based adaptive controller that guarantees the convergence of the velocity tracking errors.

Keywords: Application of nonlinear analysis and design, Tracking, Control in system biology
Abstract: This paper focuses on the mechanism of propulsion of a Purcell swimmer whose segments are magnetized and react to an external magnetic field applied into the fluid. By an asymptotic analysis, we prove that it is possible to steer the swimmer along a chosen direction when the control functions are prescribed as an oscillating field. Moreover, we discuss what are the main obstructions to overcome in order to get classical controllability result for this system.

Keywords: Stability of nonlinear systems, Lyapunov methods
Abstract: Absolute stability theory is an important and bright page in the history of automatic control theory. The ideas of its founders influenced subsequent development of control theory. The methods developed in their pioneering works formed the basis of many subsequent works on the theory of nonlinear and robust systems. In this talk the early stages of development of the absolute stability theory will be surveyed which were held in St. Petersburg, then Leningrad, in the 1940s-1960s and were primarily associated with the names of outstanding scientists Anatoly Lurie, Efim Rosenwasser, Vladimir Yakubovich. Particularly, the history of Lurie problem, Kalman-Yakubovich-Popov (KYP) lemma and circle criterion will be discussed.

Keywords: Adaptive control, Lyapunov methods, Tracking
Abstract: The purpose of the paper is to introduce to the control community the brilliant but little-known part of Yakubovich's academic heritage --- the method of recurrent objective inequalities. This method was successfully used by V.A.Yakubovich and his followers in pattern recognition, adaptive control and robotics. The paper deals with the last two topics. The most of surveyed results were published in Russian. A 1975 video about experiments with the first Soviet self-learning robot will be shown.

Keywords: Lyapunov methods, Stability of nonlinear systems, Sum-of-squares
Abstract: The Kalman-Yakubovich-Popov (KYP) lemma is a fundamental result of control theory. It has applications in many areas of automatic control and is referred in thousands of papers. Every year new versions and generalizations of the KYP lemma are published.

The paper explores three existing approaches to the proof of the lemma: algebraic methods, the use of linear-quadratic optimization, and the convex duality approach. A brief survey of the results achieved in each approach is presented, and some of the existing problems are discussed.

Keywords: Lyapunov methods, Stability of nonlinear systems, Stability of hybrid systems
Abstract: The paper gives new results that contribute to the development of a stability theory for 2D nonlinear discrete and differential systems described by a state-space model of the Roesser form using an extension of Lyapunov's method. One of the main difficulties in using such an approach is that the full derivative or its discrete counterpart along the trajectories cannot be obtained without explicitly finding the solution of the system under consideration. This has led to the use of a vector Lyapunov function and its divergence or its discrete counterpart along the system trajectories. Using this approach, new conditions for asymptotic stability are derived in terms of the properties of two vector Lyapunov functions. The properties of asymptotic stability in the horizontal and vertical dynamics, respectively, are introduced and analyzed. This new properties arise naturally for repetitive processes where one of the two independent variables is defined over a finite interval. Sufficient conditions for exponential stability in terms of the properties of one vector Lyapunov function are also given as a natural follow on from the asymptotic stability analysis.

Keywords: Control under communication constraints (nonliearity), Lyapunov methods, Networked systems
Abstract: The paper deals with observation of nonlinear and deterministic, though maybe chaotic, continuous-time systems via finite capacity communication channels.

Keywords: Lyapunov methods, Stability of nonlinear systems, Stability of hybrid systems
Abstract: This paper considers discrete and differential nonlinear repetitive processes using the state-space model setting. These processes are a particular class of 2D systems that have their origins in the modeling of physical processes. Their distinguishing characteristic is that one of the two independent variables needed to describe the dynamics evolves over a finite interval and therefore they are defined over a subset of the upper-right quadrant of the 2D plane. The current stability theory for nonlinear dynamics assumes that they operate over the complete upper-right quadrant and this property may be too strong for physical applications, particulary in terms of control law design. With applications in mind, the contribution of this paper is the use of vector Lyapunov functions to characterize a new properties termed pass profile exponential stability and pass profile asymptotic stability.

Keywords: Reinforcement learning control, Remote and distributed control
Abstract: In this paper, agents learn how often to exchange information with neighbors in cooperative Multi-Agent Systems (MASs) such that their user-defined cost functions are minimized. The investigated cost functions capture trade-offs between the MAS local control performance and energy consumption of each agent in the presence of exogenous disturbances. Agent energy consumption is critical for prolonging the MAS mission and is comprised of both control (e.g., acceleration, velocity) and communication efforts. The proposed methodology starts off by computing upper bounds on asynchronous broadcasting intervals that provably stabilize the MAS. Subsequently, we utilize these upper bounds as optimization constraints and employ an online learning algorithm based on Least Square Policy Iteration (LSPI) to minimize the cost function for each agent. Consequently, the obtained broadcasting intervals adapt to the most recent information (e.g., delayed and noisy agents' inputs and/or outputs) received from neighbors and provably stabilize the MAS. Chebyshev polynomials are utilized as the approximator in the LSPI while Kalman Filtering (KF) handles sampled, corrupted and delayed data. The proposed methodology is exemplified in a consensus control problem with general linear agent dynamics.

Keywords: Reinforcement learning control
Abstract: Adaptive dynamic programming is a collective term for a variety of approaches to infinite-horizon optimal control. Common to all approaches is approximation of the infinite-horizon cost function based on dynamic programming philosophy. Typically, they also require knowledge of a dynamical model of the system. In the current work, application of adaptive dynamic programming to a system whose dynamical model is unknown to the controller is addressed. In order to realize the control algorithm, a model of the system dynamics is estimated with a Kalman filter. A stacked control scheme to boost the controller performance is suggested. The functioning of the new approach was verified in simulation and compared to the baseline represented by gradient descent on the running cost.

Keywords: Reinforcement learning control
Abstract: In this paper, a data-driven on-policy optimal control design is proposed for continuous-time linear time invariant (LTI) systems with completely unknown dynamics. An online system identifier and control gain parameter estimator, which use past and current data together with standard gradient descent update laws, facilitate the design of an adaptive optimal controller that guarantees parameter convergence without the need of persistence of excitation (PE). Unlike the classical approach of enforcing the restrictive PE condition on the regressor, the data-driven approach is verifiable online and establishes parameter convergence from information rich past stored data simultaneously with the current data. A state feedback controller is designed using a dynamic gain parameter which is shown to converge to the neighborhood of the optimal LQR gain. Semi-global uniformly ultimately bounded (UUB) stability of the overall system is established using Lyapunov-based analysis. Simulation results further validate the developed result.

Keywords: Reinforcement learning control, Evolutionary algorithms in control and identification
Abstract: Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper addresses the problem of finding a smooth policy based on the value function represented by means of a basis-function approximator. We first show that policies derived directly from the value function or represented explicitly by the same type of approximator lead to inferior control performance, manifested by non-smooth control signals and steady-state errors. We then propose a novel method to construct a smooth policy represented by an analytic equation, obtained by means of symbolic regression. The proposed method is illustrated on a reference-tracking problem of a 1-DOF robot arm operating under the influence of gravity. The results show that the analytic control law performs at least equally well as the original numerically approximated policy, while it leads to much smoother control signals. In addition, the analytic function is readable (as opposed to black-box approximators) and can be used in further analysis and synthesis of the closed loop.

Keywords: Reinforcement learning control
Abstract: We consider the infinite-horizon optimal control of discrete-time, Lipschitz continuous piecewise affine systems with a single input. Stage costs are discounted, bounded, and use a 1 or infinity-norm. Rather than using the usual fixed-horizon approach from model-predictive control, we tailor an adaptive-horizon method called optimistic planning for continuous actions (OPC) to solve the piecewise affine control problem in receding horizon. The main advantage is the ability to solve problems requiring arbitrarily long horizons. Furthermore, we introduce a novel extension that provides guarantees on the closed-loop performance, by reusing data ("learning") across different steps. This extension is general and works for a large class of nonlinear dynamics. In experiments with piecewise affine systems, OPC improves performance compared to a fixed-horizon approach, while the data-reuse approach yields further improvements.

Keywords: Fuzzy and neural systems relevant to control and identification
Abstract: Classification problem is important for big data processing, and deep learning method named deep belief network (DBN) is successfully applied into classification. But traditional DBN is an unsupervised learning method, which leads to a gap between extracted features and concrete tasks. In this paper, a semi-supervised DBN (SSDBN) based on semi-supervised restricted Boltzmann machine(SSRBM) is proposed to shorten the gap and improve the accuracy of classification. Firstly, through introducing relevance constraint, supervised information is equivalently integrated into the learning process of restricted Boltzmann machine. Secondly, SSDBN-based model is constructed to improve the accuracy of classification problem. Finally, the proposed SSDBN is validated with hand-written digits classification standard dataset MNIST, and experimental results show that SSDBN outperforms traditional DBN and other models with respect to classification.

Keywords: well-posed distributed parameter systems, Linear systems, Time-invariant systems
Abstract: In 1968 Fattorini published in SIAM Journal on Control his well-known paper with the title ``Boundary Control Systems''. In this paper he sets up the abstract theory for systems described by partial differential equations with a control at the boundary. The theory as presented is complete, meaning that it could be copied into textbooks and articles without major alternations. In his paper and in these textbooks one finds examples illustrating the use of this abstract concept. However, as we will show, these examples are much older, and are more or less standard within the theory of partial differential equations with an inhomogeneous boundary condition.

Keywords: Infinite-dimensional systems, Control of partial differential equations, stability of distributed parameter systems
Abstract: A non-exhaustive overview is given of the major topics, related to LQ-optimal control of infinite-dimensional systems, in which significant progress has been made during the last decades.

Keywords: Asymptotic stabilization, Distributed nonlinear control, stability of distributed parameter systems
Abstract: In this talk we survey how some successful methods for the stabilization of nonlinear finite dimensional control systems have been adapted to deal with control systems modeled by means of nonlinear partial differential equations. A special emphasis is put on cases where the nonlinearities have an important role and the methods will be presented on physical equations, as the heat equation, the Navier-Stokes and the Euler equations of incompressible fluids, the Saint-Venant equations (shallow water equations), the Korteweg-de Vries equations and the Schrödinger equations. We also present a real life application, namely the regulation of the rivers La Sambre and La Meuse. Challenging open problems are also briefly described.

Keywords: backstepping control of distributed parameter systems
Abstract: In this talk, we take a brief tour through the history of backstepping as a method for boundary control of Partial Differential Equations (PDEs). We start by reviewing its origins in finite-dimensional control theory and some early works that used Volterra integral transformations. Next we recall the first seminal works of Smyshlyaev and Krstic, which allows us to present the main ingredients of the method, namely the concepts of a backstepping invertible transformation, a target system and the kernel equations, as well as dual results that allow to design observers. From these ideas, the method has been since used to design controllers and observers for many different PDE systems, including parabolic and hyperbolic systems, flows governed by the Navier-Stokes, the Ginzburg-Landau, or the MHD equations, Timoshenko beams, and others. We explore some of these results, as well as some extensions such as adaptive backstepping controllers.

Keywords: port Hamiltonian distributed parameter systems, model predictive control for distributed parameter systems, backstepping control of distributed parameter systems
Abstract: The control theory of systems governed by partial differential equations has a long history and has reached a certain level of maturity. Based on recent developments, in the following potential research perspectives are discussed that address both methods and applications. The presentation is not intended to be exhaustive and reflects the author's personal view.

Keywords: Estimation and fault detection, Fault diagnosis and fault tolerant control, FDI for nonlinear Systems
Abstract: Model-based fault diagnosis has been indicated as a fundamental method in enabling optimal maintenance of buildings, thus leading to important energy savings. Anyway, accurate models of buildings and their technical equipment are seldom available, and this lack of knowledge applies even more to descriptions of modeling and measurement uncertainties, which are needed for developing robust fault detection thresholds with given performances in terms of False Alarm Rates. In the present paper we propose to overcome both limitations, by introducing: 1) a methodology for the automatic synthesis of a global model of a building and its equipment, leveraging a purposely built ontology-based Building Information Model; and 2) a novel message passing algorithm called MP-BUP for automatically propagating the effects of uncertainties and derive robust probabilistic thresholds.

Keywords: Modeling and simulation of power systems, Model predictive and optimization-based control, Optimal operation and control of power systems
Abstract: Modern domestic heating systems combine conventional heat sources with more advanced devices such as heat pumps or energy storages. Due to the existence of multiple sources and sinks, the question arises, how to operate these systems in an economically optimal fashion. It is natural to pose this question as an optimal control problem that is solved on a receding horizon, where the receding horizon allows to account for periodically updated predictions of the heat demand and energy prices. These periodic updates imply, however, the optimal control problem has time-variant constraints and a time-variant cost function. We demonstrate the resulting receding horizon optimal control scheme can be solved and optimizes the heat flows in a domestic heating system that comprises a heat pump, a thermal storage and an auxiliary heater. Our results show the predictive controller is able to anticipate and compensate for heat shortages due to heat pump blocking times.

Keywords: Distributed Fault-tolerant Control, Reconfigurable control, sensor and actuator faults
Abstract: The paper presents a distributed Sensor Fault Diagnosis architecture for Industrial Wireless Sensor Networks monitoring HVAC systems, by exploiting a recently proposed distributed clustering method. The approach allows the detection and isolation of multiple sensor faults and considers the possible presence of modeling uncertainties and disturbances. Detectability and isolability conditions are provided. Simulation results show the effectiveness of the proposed method for an HVAC system.

Keywords: Advanced control technology, Nonlinear process control, Process modeling and identification
Abstract: This paper presents a model-based multi-variable controller for large-scale air-conditioning systems. First, a generic model of a cross-flow heat exchanger is proposed. Thereafter a control-oriented model in Takagi-Sugeno form is derived based on well-reasoned simplifications. The Takagi-Sugeno structure allows for the design of a parallel-distributed output feedback controller for the entire operating range of the air-conditioning system with respect to the outlet air temperature and humidity. An illustrative example with common requirements and ambient conditions demonstrates the effectiveness of the proposed controller scheme.

Keywords: Distributed Fault Diagnosis, Applications of FDI and FTC, FDI for nonlinear Systems
Abstract: This paper presents a model-based methodology for diagnosing actuator and sensor faults affecting the temperature dynamics of a multi-zone heating, ventilating and air-conditioning (HVAC) system. By considering the temperature dynamics of the HVAC system as a network of interconnected subsystems, a distributed fault diagnosis architecture is proposed. For every subsystem, we design a monitoring agent that combines local and transmitted information from its neighboring agents in order to provide a decision on the type, number and location of the faults. The diagnosis process of each agent is realized in three steps. Firstly, the agent performs fault detection using a distributed nonlinear estimator. After the detection, the local fault identification is activated to infer the type of the fault using two distributed adaptive estimation schemes and a combinatorial decision logic. In order to distinguish between multiple local faults and propagated sensor faults, a distributed fault isolation is applied using the decisions of the neighboring agents. Simulation results of a 5-zone HVAC system are used to illustrate the effectiveness of the proposed methodology.

Keywords: Model predictive and optimization-based control
Abstract: This paper aims at transferring the benefits reported on model predictive control (MPC) for office buildings to residential buildings. A modular modeling approach for heating systems and a modified control design is proposed. The advantages of the attempt of particularly taking the hydraulic domain into account in the MPC law are shown and a comparison with a standard controller is done via simulation.

Keywords: Automatic control, optimization, real-time operations in transportation, Modeling and simulation of transportation systems, Transportation logistics
Abstract: Congestion in the air traffic network is a problem with an increasing relevance for airlines costs as well as for airspace safety. One of the major issue is the limited capacity of the air network, that is the limited maximum number of aircraft that can coexist on some points of the network. This value can also vary during the day for safety constraints (weather and visibility). In this work an Autonomous Agent approach is proposed to solve in real time the problem of air traffic congestion. The air trac network is divided in different sectors each one modeled with a graph. Each sector has its own control decision agent managing the air traffic involved in it. When a congestion occurs in a sector the control decision agent has to reschedule and/or reroute the flights with the aim of respecting capacity constraints and of limiting delays. In this paper rerouting solutions are obtained by applying an iterative procedure based on a generalized Dijkstra algorithm on the graph. We test the effects of different levels of cooperation between agents controlling adjacent sectors while solving the congestion problem.

Keywords: Automatic control, optimization, real-time operations in transportation, Intelligent transportation systems, Multi-vehicle systems
Abstract: The problem of maximizing the probability of two trucks being coordinated to merge into a platoon on a highway is considered. Truck platooning is a promising technology that allows heavy vehicles to save fuel by driving with small automatically controlled inter-vehicle distances. In order to leverage the full potential of platooning, platoons can be formed dynamically en route by small adjustments to their speeds. However, in heavily used parts of the road network, travel times are subject to random disturbances originating from traffic, weather and other sources. We formulate this problem as a stochastic dynamic programming problem over a finite horizon, for which solutions can be computed using a backwards recursion. By exploiting the characteristics of the problem, we derive bounds on the set of states that have to be explored at every stage, which in turn reduces the complexity of computing the solution. Simulations suggest that the approach is applicable to realistic problem instances.

Keywords: Decentralized Control and Systems, Automatic control, optimization, real-time operations in transportation, Modeling and simulation of transportation systems
Abstract: Actuation failures in perimeter control might cause severe system performance degradation, if the control policy is not designed to compensate these failures. Current aggregate traffic models and perimeter control strategies do not integrate the effect of component failures, and cannot compensate them. The component failures are uncertain in nature, and very often it is impossible to predict in advance which component may fail during system operation, and when the component failure occurs. In the current paper, an adaptive fault-tolerant distributed control scheme is developed for a multi-region MFD system model. The control goal is to achieve a desired tracking performance with simultaneous (i) actuating control components failures, (ii) model parameters uncertainties, and (iii) unknown demand disturbances.

Keywords: Automatic control, optimization, real-time operations in transportation, Modeling and simulation of transportation systems, Intelligent transportation systems
Abstract: This paper proposes an innovative control scheme for regulating traffic in freeway networks via ramp metering in order to reduce congestion and traffic emissions. The control scheme is composed by a supervisor, acting at a higher level, which receives measurements from the whole network and periodically makes a prediction on the system evolution, and by local feedback controllers, acting at a lower level. These latter are textit{extended} PI-ALINEA controllers which compute the control action not only on the basis of measurements immediately downstream the on-ramp, as in standard PI-ALINEA, but also on the basis of other measurements or predictions referred to farther locations which are time-varying and communicated by the supervisor. The supervisor decides when the control law of textit{extended} PI-ALINEA controllers needs to be changed according to an event-triggered logic.

Keywords: Transportation logistics, Freight transportation, Coordinated control
Abstract: The production inventory routing problem is an integrated supply chain planning problem where the decisions concerning production, inventory and routing are simultaneously determined. We present an extended production inventory routing model dealing with the perishable food where the quality is explicitly formulated. We adapt a two phase iterative approach to solve the propose model. The model is first decomposed into two sub-problems and solved sequentially, then an iterative procedure is applied to remedy the flaw of the decomposition method. The computational results on randomly generated instances with up to 50 retailers show that we can obtain good quality solutions within acceptable time.

Keywords: Adaptive and robust control in marine system, Autonomous surface vehicles, Neural networks
Abstract: In this paper, a neural network-based adaptive control algorithm is proposed for a class of non-affine systems where the nonlinear influence of the system input on the states is unknown. The algorithm transforms the problem of controlling non-affine systems to control of nonlinear affine systems and then, by approximating the inverse of the input function, calculates feasible control input. Lyapunov technique, Uniform Ultimate Boundedness and Matrix Singular Values are used for stability analysis and design of the controller. In order to investigate the performance of the algorithm, it is applied to an autonomous vessel where the dynamics of the propeller is unknown.

Keywords: Adaptive system and control, Robust adaptive control, Nonlinear adaptive control
Abstract: A recent paper revisited and modified the use of various components of the simple adaptive control (SAC) approach of LTI systems and showed how one can use passivity concepts such that, while it maintains robustness with disturbances, it also allows asymptotically perfect tracking in ideal conditions. Using the extension of passivity conditions to nonstationary systems, this paper extends the analysis of robustness with disturbances and asymptotically perfect tracking in ideal conditions to nonstationary and nonlinear systems.

Keywords: Adaptive system and control, Adaptive control -applications, Robust adaptive control
Abstract: The paper deals with control architectures when a Simple Adaptive Control (SAC) based subsystem is a part of a linear closed loop. The main issue of such control architecture is the stability. It is proved that a linear closed loop with a SAC based subsystem remains stable and the performance converges to a respective linear closed loop. Two cases with different control architectures are considered in the paper: (i) Three-loop missile autopilot with a SAC based fins actuator; (ii) A target tracking loop where the classical SAC architecture cannot be implemented per se and the model to be tracked is the target state estimator. The performance of these architectures is demonstrated by simulations.

Keywords: Model reference adaptive control
Abstract: The paper describes the simple adaptive algorithm for linear time-invariant plants with any relative degree, input delay and bounded disturbances. Synthesis of the control system is based on the modified high order tuners. The proposed algorithm ensures required accuracy of difference between the plant output and the reference signal, which significantly depends on external disturbances. The simulations illustrate the effectiveness of the designed algorithm.

Keywords: Adaptive system and control, Control over networks
Abstract: We study passification-based adaptive control of minimum-phase systems with sampled measurements. First, we remove the "relative degree one" assumption previously imposed in Selivanov et al. (2015). To achieve this, we introduce the shunting method that allows to obtain hyper-minimum-phase augmented system, which is further stabilized by a passification-based adaptive controller. Second, we introduce a switching event-triggering mechanism to reduce the number of transmitted measurements. The advantage of adaptive control and the benefit of the switching approach are demonstrated by an example of adaptive flight control.

Keywords: Adaptive control -applications, Filtering and smoothing, Adaptive system and control
Abstract: In this paper tracking of an unknown multi-sinusoidal signal with fast online frequency estimation is addressed. The latter has been achieved by the so-called dynamic regressor extension and mixing (DREM) approach, which allows to increase performance of multiple frequency estimation. Designed predictive compensation algorithm is capable to operate under presence of computational or transmission delays. The resultant controller has been implemented to a robotic arm with a computer vision system. An extensive experimental study shows undoubted advantages of the DREM-based frequency estimator in comparison to the classical gradient approach. A detailed analysis of the transient response properties is provided in the paper.

Keywords: Adaptive control -applications, Robust adaptive control
Abstract: Recently developed results for direct adaptive control design in the context of uncertain systems in descriptor form are discussed and applied to a satellite attitude control problem. Thanks to descriptor model handling the methodology allows to address with convexity arguments uncertainties on the inertia matrix. The design of the adaptive law is done with semi-definite programming tools (LMI solvers) and is applicable with the sole knowledge of a controller stabilizing the nominal system (no passivity assumptions). The tackled satellite model is linear (assuming small pointing errors), full 3D rotations are considered (axes are coupled), and mixed reaction wheel and magnetotorquer actuation is adopted. Compared to the provided initial LTI control law, adaptive control increases robustness margins by a factor 262%.

Keywords: Motion Control Systems, Mechatronic systems, Design methodologies
Abstract: Hydraulic driven manipulators face serious control problems due to the nonlinear system dynamics and model and parametric uncertainties of hydraulic actuators. In this paper, a novel sensor-based Incremental Nonlinear Dynamic Inversion controller is applied to force tracking control of hydraulic actuators of a hexapod flight simulator motion system, which together with an outer-loop motion tracking controller forms a motion control system. Due to the use of feedback of pressure difference derivatives, the proposed technique is not dependent on accurate model and parameters, which makes the controller inherently robust to model uncertainties. Furthermore, The sensor-based control approach is particularly suitable for hydraulic force tracking in existence of an outer-loop controller decoupling hydraulic-mechanic interaction term from the inner-loop dynamics. Simulation results indicate that the novel approach yields better tracking performance and confirm the greater robustness to model and parametric uncertainties compared with a traditional nonlinear dynamic invention approach.

Keywords: Motion Control Systems, Design methodologies
Abstract: The paper deals with a noise attenuation design of a modification of the disturbance observer (DO) based PI control for an AC servo drive with the dominant first-order dynamics. The achieved results are compared with the traditional PI control. The DO based filtered PI (DO-FPI) controller consists of the P control with adjustable filter. It is augmented with an integral action based on load reconstruction using equally filtered plant dynamics inversion. The availability of broadly scalable filters requires a simple one-parameter based tuning. In looking for a possibly good noise attenuation, it allows changing the filter order and time constants by keeping nearly the same speed of disturbance responses as in the PI control. An application to the speed servo control with an incremental position sensor brings to light interesting properties of the compared solutions. The DO FPI and PI control give nearly equally robust solutions. However, in case of PI control it is at the expense of significantly increased torque ripple.

Keywords: Motion Control Systems, Design methodologies
Abstract: This paper studies the smooth and precise speed regulation of DC servo system subject to both unknown external disturbances and internal uncertainties. we first propose a disturbance observer based switching control scheme, consists of two DOBs and a switching logic. Thereafter, a novel switching mechanism is meticulously designed to switch the different DOBs according to the variation of output error and settling time, and the stability of the scheduled system is analyzed through Lyapunov stability theory. Experiments on speed regulation show that both smooth dynamic responses at transient state and high accuracy performance at steady state are achieved and the effectiveness of the proposed control scheme is verified.

Keywords: Motion Control Systems, Mobile robots, Guidance navigation and control
Abstract: This work has examined the use of navigation function (NF) for the motion task of parallel parking performed by mobile robot with kinematic constraints working in two-dimensional environment with obstacles. Classic navigation function approach assumes omnidirectional robot. In this paper NF extended with orientation component was tested. It can be tuned to guarantee global convergence to the target position with desired orientation. This paper provides results of experiments with simulated as well as physical robot, showing typical behavior of the method, as well as authors' remarks on its benefits and disadvantages.

Keywords: Motion Control Systems
Abstract: Auto-tuning of robust PID Control is presented. To this end, we propose the equivalence condition between the modied TDC using sliding mode concept and the PID controller in a discrete-time domain. To realize auto-tuning of the PID control gains, we have synergistically combined the sliding mode concept and adaptive control. The desired error dynamics is described by using sliding mode concept, and the adaptive gain dynamics is proposed using the sliding variable. A leakage term is used for the gain dynamics to cope with the noise effect. The gain automatically increases when the sliding mode error is large, and decreases when sliding mode error is relatively small. The tracking errors of the proposed method are almost identical compared with those of well-tuned constant gains. Consequently, auto-tuning of the gains for the PID controller is realized instead of manual tuning.

Keywords: Motion Control Systems, Flying robots, Modeling
Abstract: This paper is focused on the tracking control problem of two quadrotors carrying one cable-suspended payload. The paper main contributions include a dynamic model constructed by Euler-Lagrange equations for this system, an iterative linear quadratic regulator (iLQR) optimal controller, which is able to track the desired trajectory for the payload position and attitude and a simulation for tracking a spiral trajectory. The nonlinear dynamic model is linearised by considering the angles of connected links from the payload to each quadrotor as the equilibrium point. The iLQR control algorithm is developed for the full system consisting of two quadrotors connected with a point mass load in order to improve the tracking performance and avoid the slung load swing. The simulation results demonstrate that the iLQR approach is able to improve the tracking performance compared with an LQR controller.

Keywords: Cyber-Physical Systems, Design methodologies, Modeling
Abstract: The deployment of control algorithms into software-based controllers is fundamental for the correctness of the designed application. Deployment involves both the configuration of design choices and the management of unpredictable behaviors of controllers. Tools and methodologies must be identified for supporting the activity. This paper proposes to break-down the deployment process into layers of abstraction. Simulations and modeling patterns are introduced for configuring the design choices of each abstraction layer. However, the verification of the approach through the application to real case studies will be matter of future works.

Keywords: Engineering methods for HMS
Abstract: The increasing demand for highly customizable manufacturing systems leads to an extreme number of possible machine variants. Feature models are often used to manage this system diversity. The development and maintenance of feature models are error-prone and time-consuming tasks, especially considering industrial-size models with thousands of features. In many cases, engineers might want to focus only on a few features relevant for their own domain. Additionally, each change may lead to anomalies in the feature model. In this paper, we present an approach to provide engineering support by giving user-friendly explanations for hidden dependencies and anomalies in feature models.

Keywords: Robots manipulators, Cyber-Physical Systems
Abstract: In this paper, a method for evaluating robots as parts of a CPPS based on their abilities is presented. The relationship between the CPPS characteristics and robot abilities is determined. The minimum level of robot abilities is determined in order to have a positive influence on CPPS. The proposed mapping is used to evaluate the anatomy of three types of open chain robots: fixed anatomy, reconfigurable and metamorphic robots. Metamorphic robots, a class of modular open chain manipulators, are found to be better than the other two types. However, their superiority comes with increased complexity of the system.

Keywords: Modeling, Mechatronic systems, Identification and control methods
Abstract: Due to different trends in manufacturing, existing mechatronic systems, such as automated manufacturing machines, are obliged to evolve in order to cope with a quickly changing environment. The evolution of mechatronic systems is achieved by adaptation processes, where changes in the physical structure of a system and/or its automation software are performed. An important phase of an adaptation process is the modeling and analysis of existing interdependencies between system elements which serve as a basis for subsequent adaptation phases. However, current modeling approaches mostly focus on single system element types and do not regard the holistic system, i.e., products, processes and resources. Accordingly, in this contribution a concept for modeling interdependencies between products, processes and resources is proposed with the aim of providing a solid data base for the generation of adaption options. The application of the concept is demonstrated by an illustrative example of a pick-and-place unit.

Keywords: Design methodologies, Mechatronic systems, Modeling
Abstract: This paper presents a Python framework for the preliminary design of embedded mechatronic systems. Mechatronic systems have the particularity to involve several levels of design and several technologies. In addition, embedded systems introduce to the design problem specific constraints like energy consumption, resistance/impact to/on environment, geometrical integration and reliability. In order to support the designer in satisfying these constraints, a Model-Based Design methodology and the corresponding framework is proposed. Models used during preliminary design of such multi-domain systems come from several disciplines and have different scales: distributed parameters (3D FEM, 3D CFD) for local level, lumped parameters (1D/0D, ODE/ADE) and state machine for global level. The dynamic simulation through 0D-1D models of the system to be designed is commonly used to validate architectural choices and preliminary sizing that requires multi-disciplinary optimization. Unfortunately, optimization can not only apply to the system level and resort to 3D models cannot be avoided, even during the early design phases. The proposed framework aims at implementing the optimization loops at both local and global levels in an open source environment. The proposed framework’s features will be underlined through a case study - a rotary electromechanical actuator for primary flight control.

Keywords: Mechatronic systems, Modeling
Abstract: Automated production systems (aPSs) are often in operation for several decades. Due to a multiplicity of reasons, these assets have to be maintained and modified over the time multiple times and with respect to multiple engineering domains. An increased economic pressure demands to perform these tasks in an optimized way. Therefore, it is necessary to estimate change effects with respect to multidisciplinary interdependencies, required surrounding non-functional tasks and the effort and costs included in each step. This paper outlines available cost estimation methods for PLC-based automation and Information Systems (ISs). We introduce Karlsruhe Architectural Maintainability Prediction for aPS (KAMP4aPS), an approach to estimate the necessary maintenance tasks to be performed and their related costs for the domain of aPSs by extending KAMP, which is limited to change propagation analysis on ISs. KAMP requires a metamodel to derive these tasks automatically. Unfortunately, a domain spanning metamodel is missing for aPSs. Hence, we need to develop a part of the metamodel derived from an AutomationML description for the chosen demonstrator at first. Finally, we apply and compare different estimation methods and KAMP4aPS to analyze the exchange of a fieldbus system as exemplary change scenario on a lab size plant to demonstrate the benefits of our discipline-spanning approach.

Keywords: Power systems stability, Control system design, Analysis and control in deregulated power systems
Abstract: In this paper, a nonlinear excitation control scheme is proposed to enhance transient stability and voltage regulation of higher-order synchronous generators. The controller is designed based on an adaptive backstepping method and the fifth-order dynamical model of synchronous generators is used to design the controller. The proposed controller is designed recursively to adapt some unknown stability sensitive parameters of synchronous generators and these unknown parameters are estimated through adaptation laws based on the formulation of control Lyapunov functions (CLFs). The proposed control scheme is also capable to overcome the over-parameterization problems of unknown parameters. Finally, the effectiveness of the proposed scheme is evaluated on a test single machine infinite bus system by applying a symmetrical three-phase short-circuit fault at different locations of the test system and compared to that of an existing adaptive backstepping controller. Simulation results demonstrate the superiority of the proposed control scheme over the existing controller in terms of settling time and providing additional damping into the system.

Keywords: Optimal operation and control of power systems, Analysis and control in deregulated power systems, Real time simulation and dispatching
Abstract: The system frequency of a power systems is a good indicator of the networks resilience to major disturbances. In a completely deregulated setting, for example in the Nordic power system, the system operator controls the system frequency manually by calling-off bids handed in to a market, called the regulating market.

In this paper we formulate the problem of optimal bid call-off on the regulating market, that the system operator is faced with each operating period, as an optimal switching problem with execution delays.

As general optimal switching problems with execution delays are computationally cumbersome we resort to a recently developed suboptimal solution scheme, based on limiting the feedback information in the control loop.

Keywords: Optimal operation and control of power systems, Intelligent control of power systems, Control of renewable energy resources
Abstract: To balance the power supply and demand with optimized control cost and nominal synchronized frequency, we propose a secondary frequency control approach, named Power-Imbalance Allocation Control (PIAC), for power systems with lossless networks, consisting of synchronous machines, frequency dependent power sources and passive loads. With Proportional-Integral control, the power imbalance is estimated by a coordinator with aggregated frequency deviations and the control inputs are optimally allocated to the controllers after solving an economic power dispatch problem on-line. The advantage of the approach is that the estimated power imbalance converges to the actual power imbalance exponentially with neither overshoot of control inputs nor unnecessary oscillations of the frequency. In addition, the convergence speed only depends on a control coefficient which is independent of any other parameters of the power systems and of the economic power dispatch problem.

Keywords: Power systems stability, Modeling and simulation of power systems, Control of renewable energy resources
Abstract: With the increase of wind energy integration in power systems, frequency control meets new challenges due to the uncertainty and stochasticity of wind power. In this paper, in respect to the frequency instability problem caused by disturbances of wind power, we propose an extended partial observer based load frequency control (LFC) scheme. An extended partial states observer (EPSO) is first designed to estimate both the unmeasurable states of the LFC system and wind power disturbance. And then a composite feedback controller using estimation from EPSO as well as measurable states of the LFC system itself is designed, thus attenuating the wind energy disturbance in the output channel and achieving asymptotical stability of both frequency and tie-line interchange power of the control area. The proposed LFC scheme has better feasibility to practical application since it is in essence a linear combination of measurable states values and EPSO-observed states values. Besides, it has better control performance than PI controller and saves the lengthy computational work of parameters tuning for PI controller with the aid of pole placement technique.

Keywords: Smart grids, Optimal operation and control of power systems, Modeling and simulation of power systems
Abstract: We consider the problem of designing distributed generation and demand control schemes that provide ancillary service in primary frequency regulation in power networks such that stability and fairness in the power allocation can be guaranteed. It is desirable in such schemes that load side participation in frequency control is activated only when the frequency exceeds prescribed thresholds so as to avoid frequent intervention in the operation of loads. This, however, leads to nonlinear control schemes with vector elds with discontinuous derivatives. In this paper, we investigate how stability and optimality may be ensured when such dynamics are considered. We show that subgradient methods can be used to derive decentralised conditions that ensure optimality of the equilibrium points. decentralised conditions for asymptotic stability that are valid in this context are also provided. We illustrate our results with simulations on the IEEE 68 bus system, where it is demonstrated that the inclusion of controllable loads offers improved transient behaviour and that an optimal power allocation among controllable loads is achieved.

Keywords: Modeling and simulation of power systems, Control system design
Abstract: We propose a new control approach for swing oscillation suppression in large power grids, based on a spatially continuous representation of the system. Traditional modeling is spatially discrete irrespective of the grid size, perceiving the swing dynamics as frequency and phase oscillations. However, we show that for long chains of generators the overall swing dynamics is essentially governed by propagating electro-mechanical waves, retrieving the oscillations only locally. We therefore model the system by partial differential equations, where we decompose the grid into open and closed ended chains of generators, strings and rings. The key principle of the proposed approach is generation of uni-directional control waves that achieve closed loop matching of measured disturbance waves. While in strings this is naturally implemented by actuation at the boundary, the lack of boundaries in rings poses significant challenges. We solve the problem by introducing a new method for interior generation of near exact uni-directional waves. The method, which we denote by Interior Wave Suppression control, requires a minimal number of concentrated actuation and measurement devices for a given ring.

Keywords: Developments in measurement, signal processing
Abstract: Noninvasive glucose monitoring is a desired objective in diabetes therapy and monitoring the glucose levels in the tear fluid is one possibility to approach this objective. The plasmonic glucose sensor presented within this work consists of metal nanostructures which cause a resonance in the optical response and the resonance wavelength position depends on the refractive index of the surrounding. Glucose selectivity is guaranteed by functionalizing the sensor with a hydrogel that swells with glucose and thus changes its refractive index. The focus of this work lies on the relationship between the refractive index and the resulting transmission spectra of the sensor. Based on this understanding, an estimation algorithm is presented, which provides a refractive index value for any measured spectrum. It is shown that this new estimation algorithm improves previous results. For a mobile application of the sensor, the transmission can only be measured at few discrete wavelengths rather than the whole spectrum. The choice of these discrete wavelengths has a huge influence on the estimation results. The developed estimation algorithm is used to build an objective function to determine the best wavelengths via sequential forward feature selection. The results of the feature selection show that even reducing the discrete wavelengths to a number of four, refractive index estimation can be performed with a relative error below 0.05%.

Keywords: Bio-signals analysis and interpretation
Abstract: This paper presents a four-stage localization method for epileptic seizure onset zones (SOZs). This method combines the advantages of both the Shannon-entropy-based complex Morlet wavelet transform (SE-CMWT) and adaptive-genetic-algorithm-based matching pursuit (AGA-MP) methods to achieve satisfactory localization performance. The Hilbert transform method is used to identify events of interest. The SE-CMWT-based power spectral density method is applied to acquire channels of interest (CoIs). By using the Morlet wavelets as the dictionary elements, the AGA-MP method combined with Morlet wavelets is developed to detect high-frequency oscillations (HFOs) on CoIs with faster speed and higher accuracy than the AGA-MP method. And the characteristics of HFOs are used to localize SOZs based on the half-maximum method. A comparison shows that our method has higher sensitivity and specificity than four existing methods.

Keywords: Physiological Model, Knowledge modelling and knowledge based systems, Control in system biology
Abstract: The paper presents a model that includes a whole-body bio-energetic analysis of the cyclist and bike dynamics. It could be used to solve control-design problems for pedelec systems. The behavior of some physiological variables during cycling is reproduced by keeping an energy aware transfer flow. The modeling approach considers three main levels: i) physiological, ii) bio-mechanical and iii) pure-mechanical. Physical laws of energy/mass conservation were applied to allow simulate the ways in which energy is stored, transferred and dissipated at each level.

Keywords: Control of physiological and clinical variables, Pharmacokinetics and drug delivery
Abstract: The benefits of closed-loop control of anesthesia in terms of drug usage, robustness to inter-patient variability and postoperative outcomes have been demonstrated in a number of clinical studies. However, to obtain regulatory approval for such systems to be employed as medical devices in operating rooms, patient safety must be demonstrated. This paper formalizes a previously published safety system for closed-loop anesthesia using formal model verification techniques. This safety system specifies safety constraints on the patient states based on the therapeutic window of propofol. To verify the feasibility of the safety constraints in all situations, a finite number of simulation scenarios can be performed. However, the formal methods verify the feasibility problem for all possible admissible inputs and states without the need for simulation. The formalized safety system for closed-loop anesthesia guarantees that the patient states stay within safety constraints.

Keywords: Chronic care and/or diabetes, Parameter and state estimation, Monitoring
Abstract: As it is already known, patients under physiological stress, i.e., during a stay at an intensive care unit (ICU), often experience stress-induced hyperglycemia and insulin resistance even if they are not diagnosed with diabetes. In order to perform tight glycemic control (TGC) either by human intervention or by means of an automatic insulin infusion system, a proper monitoring system of the critical variables needs to be available. In this paper, the problem of estimating the state of the glucose-insulin system for a critically ill patient is investigated. Unlike most contributions, the interstitial glucose is assumed to be continuously measured by a continuous glucose monitor (CGM) instead of plasma glucose which is not usually available in most clinical facilities. Moreover, the measurement noise is characterized by a non-Gaussian noise as is suggested in recent studies. Three nonlinear filters are investigated, the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), and the Particle Filter (PF), under three different scenarios.

Keywords: Medical imaging and processing, Biomedical and medical image processing and systems, Quantification of physiological parameters for diagnosis and treatment assessment
Abstract: Electrical impedance tomography (EIT) is an promising imaging technology for continuous bedside monitoring of ventilation and perfusion. However, due to the spatial and frequency overlapping of ventilation and cardiac components in the heart-lung interaction system, it's difficult to separate the components in spontaneous breathing subjects. We introduces an intuitive method based on multi-dimensional ensemble empirical mode decomposition to explore the intrinsic oscillation modes of the ventilation and cardiac components from EIT data. This study combines the spatial information with temporal information, and establishes the combination strategy for the two physiological components based on multi-scale analysis. Our study presents preliminary in-vivo results from two healthy male subjects, and qualitatively validates the efficiency of resolving the overlapping of ventilation and perfusion components. The method proposed in our study is believed to open up new possibilities for the assessment of lung ventilation and perfusion.

Keywords: Linear systems, Time-invariant systems, Parametric optimization
Abstract: A novel multi-objective control problem with generalized H_2 criteria is studied for a linear system with a single disturbance input and multiple objective outputs. The generalized H_2-norm is the L_2 to L_{infty} gain of the system with the L_{infty}-norm, understood in a broader sense than usual. Necessary conditions for Pareto optimality are derived in this paper. By using Germeyer convolution it is established that Pareto optimal controllers can be synthesized in terms of LMIs as the generalized H_2-optimal controllers for the closed-loop system with the single input and the single output consisting of the scaled objective outputs. Vibration isolation and attenuation problems are considered in a novel framework as applications of the approach proposed. Pareto optimal isolators and attenuators are synthesized for two basic models in the problems of optimal protection from vibration.

Keywords: Evolutionary algorithms
Abstract: In this paper a technique for identifying a first principles model of a Peltier cell using Multi-Objective Evolutionary optimization and experimental data is proposed. The methodology of multi-objective optimization applied together with the method of representation of the Pareto front called level diagram enables the analysis of the set of models obtained for the temperatures of a Peltier cell in different scenarios. Level diagrams facilitate the selection of a compromise model to characterize the thermoelectric module successfully. The main advantage of the multi-objective design methodology is to easily understand the trade-off among several Pareto optimal solutions. The designer can select the final solution according to his/her preferences without missing the knowledge available from the set of optimal solutions.

Keywords: Evolutionary algorithms, Linear multivariable systems, Parametric optimization
Abstract: In this paper a new multi-objective approach for loop pairing in multi-input/multi- output processes is studied. Each controller structure of proposed input-output pairing is considered as a possible design concept. Under the meaningful objective space, defined by the control engineer, the concepts are compared and a final controller with a given trade-off is chosen. By means of an example, the paper establishes several findings: first, the controller type can be relevant when deciding on possible pairings and second, it is even more relevant the objectives chosen by the designer and his/her preferences. These findings point to multi-objective approach pairing as a powerful and promising technique.

Keywords: Evolutionary algorithms, Process control
Abstract: This papers presents a new heuristic multiobjective optimization procedure based on Kalman filtering. The novel approach is compared to the well-known Nondominated Sorting Genetic Algorithm version II with the Zitzler, Deb and Thiele test suite showing favorable results for the Kalman based solution. Further improvement on the proposed algorithm is made with respect to the learning factor, which is set to be time-dependent, improving the algorithm in most cases and diminishing the number of control parameters. The proposed multiobjective optimization algorithm is interesting due to its convergence and coverage quality, its computational complexity and few parameters to be adjusted.

Keywords: Evolutionary algorithms
Abstract: This paper presents a complete (charging, discharging and thermal characteristics) non-linear model identification of a lead acid battery by using a multi-objective (MO) approach. Batteries are a very important part for a lot of processes, hybrid electric vehicles (HEV) are an example. In an HEV, the battery model is critical because defines the electrical behaviour of the system and directly influences the control strategy. The batteries can be charged and discharged at any time, so a complete battery model to simulate the vehicle behaviour and its control is needed. This paper presents a solution to this problem, taking into a account accuracy (the best fits batteries dynamics behaviour) and the robustness (non-modelled dynamics and manufacturing tolerance) properties of the model are considered simultaneously under a MO approach. Thanks to the MO approach the performance of each one of the Pareto optimal models can be analysed and a preferred model selected. In addition, an evaluation phase of the model has been done where it is observed that the model performance is accurate.

Keywords: Control in system biology, Parametric optimization, Systems biology
Abstract: Stochasticity in biological systems often referred to as gene expression noise is ubiquitous. The main sources of this noise come about in two ways. The implicit randomness of the biochemical reactions generates intrinsic noise inside the cell. Other cellular processes are themselves products that vary over time and from each cell to another, producing the so-called extrinsic noise. Controlling the mean expression level of a gene while reducing its noise is a challenge in many applications of Synthetic Biology. In previous works, we proposed a gene synthetic circuit to reduce gene expression noise while achieving a desired mean expression level. The circuit combines a negative feedback loop and a cell-to-cell communication mechanism based on quorum sensing. In this work, we use a multi-objective optimization design approach to find the best values for the tunable-in-the-lab parameters that, for a given desired mean expression value, achieve minimization of gene expression noise caused by intrinsic and extrinsic fluctuations. Our approach allows tuning the circuit parameters required to minimize noise effects, providing results which prove in accordance with genome-wide experimental data reported in the literature. Exploring different scenarios, either considering only intrinsic noise or considering both extrinsic and intrinsic ones, we find that the design strategies obtained for both cases are not transferable. Thus, designing the circuit parameters taking into account only intrinsic noise yields a sub-optimal design with decreased performance when evaluated in a scenario where both extrinsic and intrinsic noise are present.

Keywords: Control of renewable energy resources, Control system design
Abstract: We present the models used in the simulation and control of a segmented, ultralight morphing rotor (SUMR). These simplified, linear models highlight some differences between the SUMR concept and conventional rotor designs. The models also aid in controller development and turbine design. A baseline controller is presented for the SUMR. To demonstrate how simplified models can be used for system optimization, we present actuator requirements for adequate closed-loop performance. Full, nonlinear, aero-elastic simulations are performed to fine tune the baseline controller for competing objectives. Finally, avenues for future work are outlined.

Keywords: Control of renewable energy resources, Analysis and control in deregulated power systems, Control system design
Abstract: Active power control can be used to balance the total power generated by wind farms with the power consumed on the electricity grid. With the increasing penetration levels of wind energy, there is an increasing need for this ancillary service. In this paper, we show that the tracking of a certain power reference signal provided by the transmission system operator can be significantly improved by using feedback control at the wind farm level. We propose a simple feedback control law that significantly improves the tracking behavior of the total power output of the farm, resulting in higher performance scores. The effectiveness of the proposed feedback controller is demonstrated using high-fidelity computational fluid dynamics simulations of a small wind farm.

Keywords: Control system design, Control of renewable energy resources
Abstract: A robust integral backstepping controller is designed in this paper in order to control a new structure of twin wind turbines. The idea of this new concept is the free rotation of the structure face the wind without using an actuator. This challenge can achieve by creating the difference between the drag forces of the two rotors, in order to initiate a yaw rotation. The controller aims at maximizing the electrical power by an optimal orientation of the structure. A strategy based on backstepping approach is proposed.

Keywords: Control of renewable energy resources, Intelligent control of power systems, Modeling and simulation of power systems
Abstract: Wind turbine wake redirection is a promising concept for wind farm control to increase the total power of a wind farm. Further, the concept aims to avoid partial wake overlap on a downwind wind turbine and hence aims to decrease structural loads. Controller for wake redirection need to account for model uncertainties due to the complexity of wake dynamics. Therefore, this work focuses first on modeling a wind farm using an uncertain plant description and second on the design of a robust Hinf controller for closed-loop wake redirection by applying standard robust modeling and control techniques on a wind farm. The wake center position is estimated and fed back to a controller which uses the yaw actuator to redirect the wake. For several inflow conditions, step simulations are conducted and system identifications are performed to obtain multiple plant models. This set of models is used to derive a nominal plant and an uncertainty set. Both the nominal model and the uncertainty set define the uncertain plant model. The robust controller is then designed showing promising results in a medium-fidelity CFD simulation model with time-varying inflow conditions.

Keywords: Control of renewable energy resources, Real time optimization and control
Abstract: When a wind turbine operates between the cut-in and rated wind speeds, the machine is controlled to maximize wind energy capture. The extremum seeking control (ESC) algorithm has been proposed to locate and track the optimal operating condition for power maximization. This is accomplished by applying the ESC to the power signal to determine optimal values for parameters of the control law or actuator settings. Since the power signal is proportional to the cube of the mean wind speed, changes in wind conditions may lead to significant variations in the performance of the ESC algorithm. This paper demonstrates that replacing the power signal with its logarithm yields an algorithm with robust and predictable performance despite variations in the mean wind speed. The theoretical analysis of the ESC with log-of-power feedback is done using a simple differential equation model. Numerical simulations support the theory.

Keywords: Control of renewable energy resources, Optimal operation and control of power systems, Model predictive and optimization-based control
Abstract: In this paper, we extend our closed-loop optimal control framework for wind farms to minimize wake-induced power losses. We develop an adjoint-based model predictive controller which employs a medium-fidelity 2D dynamic wind farm model. The wind turbine axial induction factors are considered here as the control variable to influence the overall performance of the wind farm by taking wake interactions of wind turbines into account. A constrained optimization problem is formulated to maximize the total power production of a given wind farm. An adjoint approach as an efficient tool is utilized to compute the gradient for such a large-scale system. The computed gradient is then modified to deal with the defined final set and practical constraints on the wind turbine control inputs. The performance of the wind farm controller is examined for a more realistic test case, a layout of a 2by3 wind farm with dynamical changes in wind direction. The effectiveness of the proposed approach is studied through simulations.

Keywords: output regulation for distributed parameter systems, backstepping control of distributed parameter systems
Abstract: We solve the output regulator problem for a system described by a Neumann boundary controlled anti-stable one-dimensional Schr"{o}dinger equation by the backstepping approach. The output to be controlled is not required to be measured and its observation operator is assumed to be admissible for a certain C_0-semigroup that is related to the original system, while the measurements used for control are located at the boundary. First we show that the open-loop system is well-posed. Then we find a state feedback control law to solve the output regulator problem by the backstepping method. We design a finite-dimensional reference observer and an infinite-dimensional disturbance observer. Based on this observer, we propose an output feedback controller to achieve output regulation.

Keywords: control of hyperbolic systems and conservation laws, backstepping control of distributed parameter systems
Abstract: In this paper the boundary control of linear 2x2 hyperbolic systems with spatially-varying coefficients and dynamic boundary conditions is considered. This leads to the stabilization problem for a bidirectionally coupled PDE-ODE system. For its solution a two-step backstepping approach is developed, that yields the usual kernel equations and additional decoupling equations of simple form. The latter can be traced back to a Volterra integral equation of the second kind, which is directly solvable with a successive approximation. The results of the paper are illustrated for a heavy rope with load.

Keywords: Infinite-dimensional systems, control of hyperbolic systems and conservation laws, thermal and process control applications of distributed parameter systems
Abstract: This paper is concerned with boundary stabilization of thermoacoustic oscillations in the Rijke tube. This system consists of a vertical tube open in both ends and a heater placed in the lower half of the tube. A speaker placed under the tube is used as actuator while a microphone placed near the top of the tube provides the pressure measurement. To study this problem we consider that the mathematical model takes the form of two interconnected compartments: one for the cold zone and other for the hot zone. Each compartment is described by the linearized Euler equations of gas dynamics and the interaction between these compartments is given by the heater model. The control input is applied on the left boundary condition of the cold zone. From this model we derive an irrational transfer function to design a stabilizing boundary control in the frequency domain. In particular, we derive necessary and sufficient conditions for the input-output stability through the use of Nyquist-type test. Experimental results show the effectiveness and real-life applicability of the method.

Keywords: control of hyperbolic systems and conservation laws, infinite-dimensional multi-agent systems and networks, Input-to-State Stability
Abstract: This paper deals with a fluid-flow modeling under compartmental representation of a network. The motivating example is a communication network made up of buffers and transmission lines where densities and flows of packets are viewed as macroscopic variables respecting the conservation laws. The main contribution lies in the resulting model. It is a coupled linear hyperbolic partial differential equations (PDEs) with an ordinary differential equation (ODEs) along with a dynamic boundary condition. Input-to-State Stability of an optimal equilibrium is analyzed using Lyapunov techniques.

Keywords: Asymptotic stabilization, stability of distributed parameter systems
Abstract: The aim of this paper is to study regional stabilization for a class of bilinear systems evolving in a spatial domain Omega. It consists in studying the asymptotic behaviour of such a system in a subregion omega of Omega, then we give sufficient conditions for weak, strong and exponential stabilization on omega. Also, we concentrate on the determination of the control which ensures regional stabilization and minimizes a given performance cost. The obtained results are illustrated by simulations.

Keywords: Infinite-dimensional systems, Linear systems, well-posed distributed parameter systems
Abstract: We are concerned with the perturbation of a rather general class of linear time-invariant systems, namely emph{well-posed linear system} (WPLS), under additive linear perturbations seen as feedback laws. Let Sigma be a WPLS with (A,B,C) as generating triple. For all control operator E, and all observation operator F such that (A,E,F) is the generating triple of a WPLS, we prove that, if (A,B,F) and (A,E,C) are also the generating triples of some WPLS, for all admissible feedback operator K for (A,E,F), we can construct a WPLS Sigma^K whose generating triple is (A^K,B^K,C^K), where A^K is the infinitisemal generator of the closed-loop of (A,E,F) by the feedback operator K. Furthermore, we give necessary and sufficient condition such that exact controllability persists from Sigma to Sigma^K. In particular, we show that this is the case for all sufficiently small bounded operator K.

Keywords: Hybrid and switched systems modeling, Complex system management, Reachability analysis, verification and abstraction of hybrid systems
Abstract: The behaviour of a linear switched system is strongly dependent on the sequence in which the system switches. The choice of such switching sequence may affect some basic properties of the switched system such as the stability or the state reconstruction. This paper deals with the existence of sequences allowing to recover the state of a switched linear system for which the modes are not observable. A sequence of modes introduces a huge amount of possible combinations among the parameters. This work gives extended conditions to verify the observability of a switched structured linear system where some particular combinations of parameters avoid to recover the state from the output and its derivatives. The study is carried out in a structural framework where only the knowledge of the system's structure is needed. Such conditions are particularly intuitive on the proposed graph and extend the capabilities of the classic graphical approach.

Keywords: Hybrid and switched systems modeling, Event-based control
Abstract: The notion of path-complete positivity is introduced as a way to generalize the property of positivity from one LTI system to a family of switched LTI systems whose switching rule is constrained by a finite automaton. The generalization builds upon the analogy between stability and positivity, the former referring to the contraction of a norm, the latter referring to the contraction of a cone (or, equivalently, a projective norm). We motivate and investigate the potential of path-complete positivity and we propose an algorithm for the automatic verification of positivity.

Keywords: Hybrid and switched systems modeling, Linear parametrically varying (LPV) methodologies, Nonlinear system identification
Abstract: Most of the studies related to the special class of systems called Switched Linear Parameter Varying (LPV) systems are considered in terms of multi-Lyapunov analysis and control design. This issue attracted a lot of attention since a single controller with acceptable performance may not exist for LPV systems with a large parameter variation region. Even more, the mode sequence has mostly considered a priori known and independent of the system parameters. In this paper, a methodology is investigated for the estimation of the discrete state and parameters for a class of discrete-time (DT) Switched LPV systems. A clustering technique based on an interpretation of belief functions combined to the least squares algorithm is used to estimate the scheduled parameters. The simulation example demonstrates the effectiveness of the identification method by giving results in terms of discrete state estimation, parameter estimation and output fit.

Keywords: Hybrid and switched systems modeling
Abstract: This paper deals with continuous plants subject to intrinsic pulse-modulated feedback, thus exhibiting hybrid closed-loop dynamics. The system structure implements a hybrid oscillator and arises in living organisms, e.g. when episodically firing neurons control the production of hormones in endocrine glands. Hybrid observers reconstructing both the continuous and discrete states of the hybrid plant from only continuous measured outputs are considered. They excel over the existing solutions through the introduction of two co-ordinated feedbacks of the output estimation error: one correcting the continuous state estimates and another adjusting the discrete ones. Different types of the feedback operator to the discrete estimates are analyzed. The observer design problem is reduced to synchronization of the observer solution with that of the plant. The synchronous mode of the observer is rendered locally stable by the selection of the feedback gains. Numerical illustration of the design procedure and observer performance with respect to a pulse-modulated model of testosterone regulation is provided.

Keywords: Hybrid and switched systems modeling, Switching control, Stability and stabilization of hybrid systems
Abstract: The paper deals with dead-beat stabilizability of autonomous switched linear discrete-time systems. More precisely, it is investigated with the problem of finding a condition on the sequences of switches which guarantee that the state of the system reaches the origin in finite steps. A literature overview highlights the fact that such a problem is an open problem in the general case. First, we give necessary and sufficient conditions under which such an autonomous switched linear discrete-time system is dead-beat stabilizable. Then, an algorithm is proposed for deciding when such a sequence exists and for providing the sequence. It is shown that the complexity of the test can be much lower than the exhaustive search.

Keywords: Hybrid and switched systems modeling
Abstract: In order to improve quality of nursing and caregiving services, it is important to analyze behaviors of service providers. Then, it is hard to directly analyze time series data. It is appropriate to find changing points and patterns of behaviors. In this paper, we propose a method for behavioral analysis using a switched linear regression model. First, the ICT-based system for nursing and caregiving services, which is developing by the authors, is summarized. Next, in order to analyze actual data obtained from this system, a switched linear regression model is introduced. An identification method of this model using mixed integer programming is proposed. Using this identification method, the changing points of behaviors and the trend of the behavior between changing points can be obtained. Finally, the proposed method is applied to actual data in nursing and caregiving services.

Keywords: field robotics, Mobile robots, Robotics technology
Abstract: This paper presents Thorvald II, a modular, highly re-configurable, all-weather mobile robot intended for applications in the agricultural domain. Researchers working with mobile agricultural robots tend to work in a wide variety of environments such as open fields, greenhouses, and polytunnels. Until now agricultural robots have been designed to operate in only one type of environment, with no or limited possibilities for customization. Thorvald II is a new module-based robot design that allows for vastly different robots to be built using the same basic modules, and rebuilt using only basic hand tools. The modules are designed to enable high quality robots that can quickly be customized for a given application in a given environment.

Keywords: Robots manipulators, Guidance navigation and control, Perception and sensing
Abstract: In the protective glass manufacturing industry for cell phones, the raw glass pieces need to be ground to the desired thickness with a grinder, which requires precise glass handling by human workers leading to a bottleneck in the production line. A low cost glass loading system with an industrial robot and web cameras is proposed to support human workers. Due to the hardware limitations, the precision and speed cannot be guaranteed. A two step approach is implemented to overcome these limitations. The glass is detected with non-linear geometry regression in data acquired by a web camera. After being picked up, the glass is placed accurately into the desired equally sized slot with an image-based visual servo and a dual-rate Unscented Kalman Filter.

Keywords: Robotics technology, Robots manipulators
Abstract: In this paper, a general architecture for robotized cooperative drilling of hybrid metal/Carbon Fibre Reinforced Polymer (CFRP) aeronautic stacks is presented. The main objectives are to reduce non-added value operations as dismantling, deburring, cleaning and to improve hole quality by reducing the occurrence of delamination and inter-laminar cracking. The approach to achieve such targets is the adoption of a one-shot drilling automatized system based on low cost cooperative robots. The proposed technology consists in a general robot architecture and a cooperative drilling process using only standard low-cost robots and off-the shelf components. A first robot is in charge of drilling the hybrid stack, while a second manipulator ensures the right clamping force between the parts of the stack. Both robots are equipped with force control capabilities to control the generalized forces raising during the interaction with the stack. In this perspective, thanks to the adoption of a fuzzy inference system, the tuning of the force controllers might be carried out by operators that have knowledge of the drilling process but not of control system technology. Such feature makes the solution appealing for industrial production environments. The approach is experimentally validated on a cell with two Comau SmartSix robots drilling an aluminium-carbon fibre stack.

Keywords: Flying robots, Mobile robots
Abstract: A class of tethered unmanned aerial vehicles is considered, featuring a chain of multicopter drones tethered one to the other. Differently from previous contributions in the literature, here the tethers are assumed to be elastic and to transfer traction loads only. Moreover, their length can be adjusted through controlled winches installed in the ground station and on each drone. Named systems of tethered multicopters, these devices can be used for a range of applications where both long runtime and good flexibility are required. The paper describes a model of the system, and presents a hierarchical control approach for this class of drones. The proposed control approach is tested through numerical simulations.

Keywords: Robotics technology, Intelligent robotics, Mechatronic systems
Abstract: This research aims to realize robotic planar-contour tracing of unknown object with high-speed and high accuracy. The challenge lies in the existence of uncertainties, which can be attributed to a robot itself (such as modeling errors, mechanical defects like backlash) or due to environmental issues (like calibration errors, pose fluctuation of a work piece). We propose to realize the tracing task by a non-model-based dynamic compensation approach based on the coarse-to-fine philosophy, which enables the manipulation with high speed and good accuracy simultaneously. This is achieved by adopting a methodology where a main robot is performing fast but coarse motion, while an add-on module is conducting accurate compensation of the overall uncertainties using high-speed visual feedback as well as force-torque feedback. Experiments for verifying the proposed method are conducted.

Keywords: Integrated monitoring, control and security for critical infrastructure systems, Production activity control, Process supervision
Abstract: The accumulation of factors like climate variability, epidemics and the increasing mobility of people around the world have increased the devastator impact of disasters. Furthermore, people have become more aware and exigent especially in terms of healthcare. Hence, a lot of pressure has been exercised on the healthcare system. Home Health Care (HHC), as a vital component of the healthcare system is facing manifolds crisis that can undermine its activities. This paper addresses the question of HHC vulnerability assessment. We use digraph presentation and matrix operations in order to synthetize indexes that infer the severity of crisis situations that a HHC facility could face, based on the predefined criteria. We introduce dynamic vulnerability assessment that takes into account cascading events over time. Vulnerability Priority Index (VPI) has been proposed to support decision-making related to the design of countermeasures and mitigation policies.

Keywords: Logistics in manufacturing, Integrated monitoring, control and security for critical infrastructure systems
Abstract: This paper deals with the problem to face hydrological disasters such as a flood, in a home health care structure. In France, these hospitals deliver mainly home health care by employing liberal nurses and liberal physicians (patients’ referring physicians), but they employ also salaried nurses and salaried physicians to control the quality of cares and to ensure the care continuity, respectively. The cares and the physicians' consultations are organized through home tour schedules. In the event of a flood, home tours can be compromised depending on home accessibility. A patient home can be easily or hardly accessible and the home care tour can be or not prolonged depending on the time required to reach each patient home. In the case where the patient home is invaded by the water, the patient must be evacuated to a regular hospital or to a field hospital which has been set up temporarily to receive the evacuated patients. In this paper, a decision-making tool is proposed to face the hydrological disaster i.e. to decide: which patients must be evacuated, which resources must be used to keep patients at home, and how many beds are required to host the evacuated patients. With such a tool, home health care structures can initiate the required collaborations in order to be ready to respond to such emergency crisis and plan patient evacuations during the flood.

Keywords: Hierarchical multilevel and multilayer control, Modelling and control of hybrid and discrete event systems
Abstract: Motivated by unbalanced demand for healthcare services at the General Hospitals (GH) and the Community Healthcare Centers (CHC) in a hierarchical healthcare system, we study a problem of optimally allocating specialist healthcare resources in the GH for two classes of patients. Adopting a finite-horizon dynamic programming approach, we derive the optimal booking limits policy to inform the resource allocation strategy between early low priority patients and potential future higher priority patients. Through numerical analysis and a case study, we show that the booking limits policy outperforms two widely used allocation policies, especially when the system is subject to high demand.

Keywords: Model-driven systems engineering, Enterprise Reference Models and Their Verification, Validation, and Accreditation, Enterprise modelling and BPM
Abstract: Mobile field hospitals are set up urgently to respond to all types of emergencies and to meet the health needs of people. In this case, field hospital must be efficient and respond accurately to the emergency for which it is deployed. However, this efficiency can be observed only when the field hospital is deployed during the emergency. Therefore, any deficiencies can not be corrected “on the fly” to fully respond to the emergency. As a consequence, it is interesting to assess the field hospitals before their deployment to ensure it is able to respond efficiently to the emergency. This paper focuses and illustrates the first development to assess a field hospital and based on maturity model. This work takes place within the collaborative research project “HOPICAMP”, funded by the French Unique Interministerial Fund for a new generation of field hospitals.

Keywords: Modelling and decision making in complex systems, Complex logistic systems
Abstract: Blood transfusion is among the most complex healthcare processes including a variety of interrelated sub-processes of different nature. Therefore, it is highly exposed to risks and such a fact is witnessed by the several errors and adverse events occurring every year. Although there is quite a significant body of literature about risk management in transfusion, risks connected with blood product procurement and handling have been scarcely investigated. The present work integrates three risk management tools developed in the project management and manufacturing areas to put forward a structured approach to identify and analyze causes of risks, their manifestations and effects. Its first application to a hospital ward revealed its potential to generate knowledge about blood transfusion uncertainty and the associated criticalities, thus increasing the level of maturity towards risks of healthcare organizations. Future research will refine and extend the approach by applying it to various healthcare settings.

Keywords: Production planning and control, Modelling and decision making in complex systems, Modeling of manufacturing operations
Abstract: The good running of a Home Health Care Structure implies the resolution of several complex problems. In this literature review, we give an overview of current work dealing with planning activities in Home Health Care Structures, and more particularly routing and scheduling of caregivers. Since the patient is the heart of the decision-making system, the reviewed works often consider non-standard criteria and constraints, compared with other routing problems. We focus more particularly on these non-standard and practical aspects of the considered problems.

Keywords: Model predictive and optimization-based control, Real time optimization and control
Abstract: We extend a recent methodology called multi-parametric NCO-tracking for the design of parametric controllers for continuous-time linear dynamic systems in the presence of uncertainty. The approach involves backing-off the path and terminal state constraints based on a worst-case uncertainty propagation determined using either interval analysis or ellipsoidal calculus. We address the case of additive uncertainty, and we discuss approaches to handling multiplicative uncertainty that retain tractability of the mp-NCO-tracking design problem, subject to extra conservatism. These developments are illustrated with the case study of a fluidized catalytic cracking (FCC) unit operated in partial combustion mode.

Keywords: Real time optimization and control, Model predictive and optimization-based control, Advanced control technology
Abstract: Modifier adaptation is a real-time optimization method that has the ability to reach the plant optimum upon convergence despite the presence of uncertainty in the form of plant-model mismatch and disturbances. The approach is based on modifying the cost and constraint functions predicted by the model by means of appropriate first-order correction terms. The main difficulty lies in the fact that these correction terms require the plant cost and constraint gradients to be estimated from experimental data at each iteration. Although the model used can support a significant level of approximation, it must satisfy the following two requirements: (i) a model adequacy condition related to the second-order optimality conditions must be valid at the plant optimum, and (ii) the model must have the same input variables as the plant. In this paper, we consider the case where (ii) is not verified because only a partial or incomplete model is available. We propose to approximate the unmodeled part of the system by a linear model that is identified using the same excitation that is used in modifier adaptation for gradient estimation. The approach is illustrated through the simulated example of a reaction-separation system with recycle.

Keywords: Real time optimization and control, Advanced control technology, Control of large-scale systems
Abstract: Self-optimizing control is a promising control strategy to achieve real-time optimization (RTO) for uncertain process systems. Recently, a global self-optimizing control (gSOC) approach has been developed to extend the economic performance to be globally acceptable in the entire uncertain space spanned by disturbances and measurement noise. Nevertheless, the gSOC approach was derived based on the assumption of no change in active constraints, which limits the applicability of the approach. To address this deficiency, this paper proposes a new CV selection approach to handle active constraint changes. It ensures that all constraints are within their feasible regions when the selected CVs are maintained at constant setpoints for all expected uncertainties. In particular, constraints of interest are linearized at multiple operating conditions to get better estimates of their values and then incorporated into the optimization formulation when solving the globally self-optimizing CVs. The new CV selection approach is able to ensure an improved operational economic performance without potential constraint violations, as illustrated in an evaporator case study.

Keywords: Model predictive and optimization-based control, Real time optimization and control, Batch and semi-batch process control
Abstract: Discrepancies between model and process responses typically result from modelling simplifications and assumptions. In the presence of structural model-plant mismatch, a run-to-run optimization procedure that follows a standard repetitive “two-step” approach, involving identification followed by optimization, may not converge to the process optimum. Parameter adaptation is a methodology that can be used to correct for model error at each batch run to drive the process to an optimum. Model parameters are updated during each iteration (batch) to account for changes in operating points as well as to correct for errors in the predicted gradients of the cost function and constraints. However, parameter values which results in a good model fit in terms of fitting the predicted outputs to data do not necessarily give an accurate prediction of the gradients of the cost function, which are necessary for reaching the optimum. We propose a parameter estimation objective which not only minimizes the differences between measurements and predictions but also maximizes the sensitivities of the cost function and constraint gradients with respect to the parameters. Towards these goals, we make use of set-based constraints which enforce uncertainty bounds over the model predictions. The resulting improvements in convergence to the optimum as compared to earlier studies are illustrated using a simulated case study of a penicillin fed-batch process.

Keywords: Real time optimization and control
Abstract: The steady-state performance of a parametrically or structurally uncertain system can be optimized using iterative real-time optimization methods such as modifier adaptation (MA). Here, we extend a recently proposed MA scheme in two important and novel directions. First, we accelerate its convergence, i.e., we reduce the number of potentially time-consuming and suboptimal transitions to intermediate steady states by appropriate filtering. Second, we propose an adaptation strategy to reduce conservatism related to the unknown curvature of the system’s steady-state performance curve. Moreover, we combine these two innovations and demonstrate their benefits on two numerical examples.

Keywords: Real time optimization and control, Process observation and parameter estimation, Batch and semi-batch process control
Abstract: In this paper we study a model-based time-optimal operation of a batch diafiltration process in the presence of fouling where the fouling model is adapted on-line using the set-membership (guaranteed) parameter estimation. The membrane fouling poses one of the major problems in the field of membrane separation processes. The studied objective in this paper is to determine a set of all possible combinations of the fouling parameters based on the measured outputs. For this reason guaranteed parameters estimation method is applied with two procedures to determine the best combination of the fouling parameters on-line during the separation. Model-based optimal non-linear control strategy is coupled with parameter estimation method in a simulation case study to show the benefits of the proposed approach.

Keywords: Statistical methods/signal analysis for FDI, Signal processing for FDI, Parameter estimation based methods for FDI
Abstract: In the field of the industrial signal processing it is important to provide appropriate models of analyzed data. Signals acquired on the faulty components often possess impulsive behavior resulting in nonexistence of moments, heavy tails and as such they cannot be analyzed with application of the standard methods related to Gaussian models. In past years varying methods allowing for the analysis of the impulsive data have evolved. We recall few of them such as alpha-stable distribution modeling, dependency measures connected with this distribution, namely codifference, covariation, fractional lower order covariance (FLOC). These measures when applied can detect frequency bands in which the information about the fault is present (informative frequency band) and also identify/localize the fault. Application of such measures, especially FLOC towards spectrogram will result in creation of the lag-frequency dependency map from which one can extract mentioned information. Authors provide in this paper a novel procedure for local damage detection combining local maxima enhancement with dependency analysis in the frequency bands using FLOC.

Keywords: Computational methods for FDI, Statistical methods/signal analysis for FDI, Signal and identification-based methods
Abstract: The theory of cyclostationarity provides many valuable tools for fault detection in rotating machines. Cyclostationarity indicators and bi-frequency maps help to quantify fault-related modulations of vibration signals. High quality and robustness of these tools are achieved at a relatively high computational cost. Thus, industrial application of cyclostationarity is limited to off-line analysis in the post-processing stage. In this paper an on-line updating of cyclostationary tools is investigated alternatively to the moving block approach. As an exemplary cyclostationary tool the cyclic modulation spectrum is involved. The filter bank approach is proposed in order to carry out the sample-by-sample updating. We compare properties of the newly proposed method with the one based on sliding discrete Fourier transform.

Keywords: Fault detection and diagnosis, Vibration and modal analysis, Mechanical and aerospace estimation
Abstract: During the last decades, an emerging interest has been reported on modelling rotating machinery signals as cyclostationary. Several tools, such as the Spectral Correlation Density (SCD) and the Cyclic Modulation Spectrum (CMS) have been proposed, assuming constant or almost constant rotating speed. To overcome this limitation, generalizations of SCD and CMS have been proposed that display cyclic Order versus Frequency. On the other hand the estimation of the instantaneous angular speed of the rotating machine is a crucial issue that has to be tackled before any analysis or diagnosis of the machine operating under non-stationary conditions. This operation is generally carried out thanks to a tachometer, or an angle encoder, but in some cases this information can be missing for different reasons. The goal of this paper is to combine a novel approach for the analysis of ”cyclo-non-stationary” signals based on the generalization of the indicators of cyclostationarity in order to cover the speed varying conditions, introducing a new speed-dependent angle averaging operator, with a new speed estimation method, that extracts from a vibration signal the most probable instantaneous rotation frequency without focusing on one specific order based on the a priori knowledge of the kinematics of the machine. The effectiveness of the global method is investigated on a synthesized CNS signal and on acceleration signals captured under speed varying conditions.

Keywords: Fault diagnosis and fault tolerant control, Data mining and multivariate statistics
Abstract: The problem of local damage detection for rotating machines is widely studied in the literature. In order to extract information of the damage, the approach based on the vibration signal analysis is applied very often. However, in many cases the analysis of the signal in the time domain is not sufficient. A good choice is the analysis of the signal into the time-frequency domain and extraction of the information on damage from this representation. In this paper, we propose to separate the time-frequency map (spectrogram) of the vibration signal into new partial spectrograms. We assume that one of the new time-frequency representation carries information about the damage, i.e. cyclic impulsive behavior. To perform the separation we apply the idea that is based on a very important topic in numerical algebra, which is matrix factorization. In the presented approach, Convex Non-negative Matrix Factorization (Convex NMF) is used. In the experiments, we analyze a real vibration signal from the heavy duty gearbox used in mining industry. The results obtained for real signal are compared with those obtained using the Spectral Kurtosis approach, one of the classical method used in the problem of local damage detection.

Keywords: Condition Monitoring, Analysis of reliability and safety, Process performance monitoring/statistical process control
Abstract: In the area of failure prognosis, system states are typically related to critical variables whose future evolution in time might significantly affect the health condition of the process, thus yielding into a critical failure at a particular time instant -typically referred to as the Time-of-Failure (ToF). Prognostic frameworks based on Bayesian processors, such as particle filtering, have already demonstrated their efficiency when trying to estimate the probability of failure in nonlinear, non-Gaussian, systems with stochastic operating profiles. However, even in those cases, it is still unclear how to measure the efficacy of the obtained results. For this purpose, it is first necessary to establish adequate performance metrics, and the Prognostics and Health Management (PHM) community has not found a convincing theory that could help to provide adequate performance indicators yet. This article represents a first step towards the solution of this problem by focusing on a rigorous mathematical definition of the prognostic problem, and defining novel performance metrics based on Bayesian Cramér-Rao Lower Bounds for the predicted state mean square error (MSE) conditional to measurement data and model dynamics. Furthermore, we also use these performance metrics to design a step-by-step methodology aimed at tuning the parameters of prognostic algorithms; guaranteeing that the precision of obtained results does not violate these fundamental bounds. This new metric is applied on the design of prognostic algorithms for the problem of State-of-Health monitoring on lithium-ion batteries.

Keywords: Condition Monitoring
Abstract: Unmanned Aerial Vehicles are becoming part of many industrial applications. The advancements in battery technologies played a crucial part for this trend. However, no matter what the advancements are, all batteries have a fixed capacity and after some time drain out. In order to extend the flying time windows, the prediction of the time that the battery will no longer be able to support a flying condition is crucial. This in fact can be considered as a standard Remaining Useful Life prognostic problem, similarly encountered in many fields. In this article, the problem of remaining useful life estimation of a battery under flight conditions, is tackled using four effective machine-learning techniques: a linear sparse model, a variant of support vector regression, a multilayer perceptron and a tree based algorithm. The efficiency of the overall proposed machine learning techniques, in the field of batteries prognostics, is evaluated based on multiple, experimental data from different flight conditions.

Keywords: Urban Mobility, Complexity modelling
Abstract: This paper focuses on the traffic signal control strategy which can prevent the traffic congestion by forcing the number of vehicles to not exceed the capacity of the network. Firstly, the store-and-forward approach is applied to model the transportation network into a linear discrete-time state space model. Then, by using the concept of positive invariance, we construct a state feedback traffic light control law that achieves our objective and at the same time respecting the constraints on both state and control variables. Finally, an example with eight intersections is worked out in order to illustrate the results.

Keywords: Intelligent Transportation, Connected Vehicles
Abstract: The craze for the Advanced Driving Assist Systems (ADAS) became major these last years. It leads the car manufacturers to constantly improve their functionalities and to distribute them to a large segment of consumers. Hence, the ADAS has to be adaptable to all vehicle models and above all approved by the drivers. Moreover, since safety of the passengers is at stake, performance and robustness need to be guaranteed. Designing these new systems is more and more time-consuming even though costs have to remain low. To cope with this contradiction, the main objective of my Ph. D. thesis is to construct a generic tuning tool for the ADAS’s control laws, including an environment model, and based on multi-objective optimization as well as sensitivity and robustness control quality criteria. This tool have to be flexible and systematic, adapting easily to the many performance and reliability constraints and to different cars. The approach envisaged is to consider a cost function and constraints having strong physical sense although mathematically formalized to be tractable by the optimization process. Beside, we aim to propose an economic and intuitive parametrization to the ADAS functionality considered, facilitating thus the comprehension of the result. This extended abstract investigates the recent developments on longitudinal and lateral control of a car and is considered as the basis of a forthcoming work on pre-calibration of the ADAS’s control laws in an industrial context.

Keywords: Systems Theory, Control and Automation Systems failure
Abstract: In this paper we consider the problem of non-quadratic stabilization of switched affine systems. Using a switching Lyaponuv function, LMI conditions based constructive method are proposed in order to design nonlinear switching surfaces and provide an estimation of the domain of attraction. An illustrative example is given in order to show the efficiency of the proposed method.

Keywords: University-industry cooperation for training control engineers
Abstract: The objective of this paper is to motivate the design of Radio Frequency (RF) analog filters under the LFT framework. Current increase in filter complexity makes traditional design methods outdated. New methods need then to be developed to tackle this issue. The Linear Fractional Transformation (LFT) is a general tool enabling to represent, analyse and synthesize interconnected systems. Coupling with the so-called KYP lemma, and its extensions, it allows to transpose complex analytical optimization problem into a finite dimensional convex problem under Linear Matrix Inequality (LMI) constraints. That is to say a problem which is optimally and efficiently solvable. All through this paper, illustrations are provided on how using the LFT framework for LC filter design. Finally, potential benefits of such an approach are enumerated, which are mainly based on the generic property of the LFT.

Keywords: Systems Theory
Abstract: This paper deals with the trajectory tracking problem of a quadcopter system through a two-layer optimization-based control strategy. The top layer controller employs the MPC (Model Predictive Control) method making use of the linearized translation dynamics in order to achieve the thrust and the sets of the reference angles. The lower control layer considers a combination of feedback linearization and optimization-based control. Designing a CTC (Computed Torque Control) law, the nonlinearities of the rotational dynamics are discarded, leading to a controllable linear model which is further used in a MPC framework for angle tracking. Simulations results for quadcopter trajectory tracking prove the benefits of the proposed multi-layer optimization-based control approach.

Keywords: Systems Theory
Abstract: This paper presents an observer approach for surface reconstruction using a Scanning Tunneling Microscopy device. The observer is designed on the basis of a control-based strategy recently proposed. It is here shown that even with the choice of a simple structure for this control-based observer, the surface reconstruction performances can be improved as compared with a more classical approach. Both simulation and real-time application results are given.

Keywords: Systems Theory
Abstract: An alternative method is presented for the selection of controller design points in a linearised gain scheduling control scheme. A set of linear systems obtained over the whole operating space is sectored using the pointwise gap metric. For each sector a linear system is selected that represents the sector best. It can be guaranteed that a linear controller designed with this system exists that stabilizes all linear systems inside the respective sector. Additional steps are proposed to refine these results after a first set of controllers has been tuned.

Keywords: Teaching curricula developments for control and other engineers
Abstract: This paper aims at presenting my PhD work. In the present paper, by applying the modulating functions method, the fractional derivatives of the output are exactly given by algebraic integral formulae using a recursive way, which are used to non-asymptotically estimate the pseudo-state of the system in noisy environment. Finally, numerical examples illustrate the efficiency of the proposed pseudo-state estimator.

Keywords: Teaching curricula developments for control and other engineers, Continuing control education in industry
Abstract: This paper presents a comparison in terms of accuracy and complexity between two approaches used for state estimation of linear systems: a classic Kalman filter and a guaranteed set-membership state estimation technique. The main goal of this paper is to analyze the advantages of these techniques and to combine them in the future in a new accurate and simple extension that handles system uncertainties and chance constraints. Two academic examples illustrate the main differences between the compared techniques.

Keywords: Robust estimation, Positive systems, Switching stability and control
Abstract: This paper addresses the problem of robust state estimation of switched uncertain systems subject to unknown disturbances. The proposed approach is based on switched interval observers which provide guaranteed lower and upper bounds allowing to evaluate the set of admissible values of the real state vector. The stability and cooperativity conditions of the proposed switched observer are expressed in terms of linear matrix inequalities (LMIs), witch have been established using a common quadratic Lyapunov function (CQLF). Estimation accuracy and robustness with respect to unknown disturbances is analyzed using mathcal{H}_infty objective with pole placement constraints. The proposed approach is illustrated by a numerical example.

Keywords: Dynamic games
Abstract: Two formulations of robust model predictive control (MPC) that have robustness properties with respect to bounded additive disturbance over conventional MPC are applied to Unmanned Aerial Vehicle (UAV) translational dynamics. These controllers use results from invariant sets theory. The tuning of the proposed MPC laws is studied, and their performances are compared in simulations.

Keywords: Econometric methods
Abstract: This extended abstract presents some backstepping control designs for a one-dimensional wave equation with an anti-damped boundary dynamics in presence of in-domain velocity source term. It discuses the merits and limitations of already carried out solutions and sketches directions of ongoing works. This paper is with the open invited track for the GdR MACS Young PhD researches.