Keywords: Stability of nonlinear systems, Constrained control, Nonlinear system theory
Abstract: Absolute stability attracted much attention in the sixties. Several stability conditions for loops with slope- restricted nonlinearities were developed. Results such as the Circle Criterion and the Popov Criterion form part of the core curriculum for students of control. Moreover, the equivalence of results obtained by different techniques, specifically Lyapunov and Popov's stability theories, led to one of the most important results in control engineering: the KYP Lemma.

For Lurye systems this work culminated in the class of multipliers proposed by O'Shea in 1966 and formalised by Zames and Falb in 1968. The superiority of this class was quickly and widely accepted. Nevertheless the result was ahead of its time as graphical techniques were preferred in the absence of readily available computer optimization. Its first systematic use as a stability criterion came twenty years after the initial proposal of the class. A further twenty years have been required to develop a proper understanding of the different techniques that can be used. In this long gestation some significant knowledge has been overlooked or forgotten. Most significantly, O'Shea's contribution and insight is no longer acknowledged; his papers are barely cited despite his original parameterization of the class.

This tutorial paper aims to provide a clear and comprehensive introduction to the topic from a user's viewpoint. We review the main results: the stability theory, the properties of the multipliers (including their phase properties, phase-equivalence results and the issues associated with causality), and convex searches. For clarity of exposition we restrict our attention to continuous time multipliers for single-input single-output results. Nevertheless we include several recent significant developments by the authors and others. We illustrate all these topics using an example proposed by O'Shea himself.

Keywords: Identification, Linear systems
Abstract: This article presents an identification algorithm dedicated to the estimation of the coefficients of physically-structured models. This algorithm is based on a constrained gradient-based optimisation that minimises a non-convex cost function to identify a structured model, starting from an initial black-box model. The suggested method is able to handle linear relations between the model coefficients so as to identify a model with the desired physical structure. Since the minimised function is non-convex, the initial model must be in the neighbourhood of its global minimum. The initialisation issue is discussed and a total-least-squares-based solution is suggested. These new developments are evaluated on a simulation benchmark, illustrating the identification of a mechanical system.

Keywords: Identification, Machine learning, Linear systems
Abstract: Most of existing results on regularized system identification focus on regularized impulse response estimation. Since the impulse response model is a special case of orthonormal basis functions, it is interesting to consider if it is possible to tackle the regularized system identification using more compact orthonormal basis functions. In this paper, we explore two possibilities. First, we construct reproducing kernel Hilbert space of impulse responses by orthonormal basis functions and then use the induced reproducing kernel for the regularized impulse response estimation. Second, we extend the regularization method from impulse response estimation to the more general orthonormal basis functions estimation. For both cases, the poles of the basis functions are treated as hyper-parameters and estimated by empirical Bayes method. Then we further show that the former is a special case of the latter, and more specifically, the former is equivalent to ridge regression of the coefficients of the orthonormal basis functions.

Keywords: Identification, Mechatronics, Modeling
Abstract: To achieve high-speed and high-precision control, an accurate plant model is required. Since the plant parameters may vary by an environmental change and so on, it is desirable to identify the plant parameters during positioning control. In the system identification, a pseudo random signal is in general used to fully excite the dynamics of the plant. Therefore, when the input-output data obtained from positioning control is used, the identification accuracy may be degraded. In this study, we propose methods to improve the identification accuracy by introducing appropriate pre-processes for the input-output data. Further, a method to identify the mechanical characteristic of the plant is proposed. The effectiveness of the proposed methods is evaluated by using the input-output data obtained from control experiments of a galvano scanner.

Keywords: Identification, Modeling
Abstract: This paper presents a new method for identifying partial differential equation (PDE). The goal is to exploit the interesting properties of the reinitialized partial moments (RPM) for PDE parameter estimation. This approach consists in applying integrals to find a partial derivative approximation. The obtained RPM model gives a direct continuous-time system identification and is linear with respect to the unknown parameters. In order to illustrate the application of this technique, we focus in the identification of the heat equation which represents a PDE with two variables.

Keywords: Identification, Modeling, Stability of linear systems
Abstract: An adaptive recursive stochastic output-only state space modeling approach is developed to improve the accuracy of modeling time-varying processes. The exponential weighted moving average approach is adopted to update the covariance and cross-covariance of past and future observation vectors. A novel method for adjusting forgetting factors based on the concept of angle between subspaces is proposed. To ensure stability of the identified model, we propose a constrained weighted recursive least square approach and propose a stable recursive canonical variate analysis (SRCVA) method. The performance of the proposed method is illustrated with simulation of the Tennessee Eastman (TE) process. Simulation results indicate that the accuracy of proposed SRCVA modeling method is superior to that of stochastic output-only state space modeling with canonical variate analysis.

Keywords: Identification, Modeling
Abstract: We propose and test on real data a two-tier estimation strategy for inferring occupancy levels from measurements of CO2 concentration and temperature levels. The first tier is a blind identification step, based either on a frequentist Maximum Likelihood method, implemented using non-linear optimization, or on a Bayesian marginal likelihood method, implemented using a dedicated Expectation-Maximization algorithm. The second tier resolves the ambiguity of the unknown multiplicative factor, and returns the final estimate of the occupancy levels. The overall procedure addresses some practical issues of existing occupancy estimation strategies. More specifically, first it does not require the installation of special hardware, since it uses measurements that are typically available in many buildings. Second, it does not require apriori knowledge on the physical parameters of the building, since it performs system identification steps. Third, it does not require pilot data containing measured real occupancy patterns (i.e., physically counting people for some periods, a typically expensive and time consuming step), since the identification steps are blind.

Keywords: Distributed parameter systems, Uncertain systems
Abstract: The boundary output feedback stabilization for a multi-dimensional Kirchhoff plate with boundary observation suffered from general external disturbance is considered. The active disturbance rejection control approach is adopted in investigation. By using this approach, the disturbance is estimated by a relatively independent estimator, based on an infinite number of ordinary differential equations reduced from the original PDEs by infinitely many time-dependent test functions. The isturbance is canceled in the feedback-loop. As a result, the control law can be designed almost as same as that for the system without disturbance. We show that with a time varying gain properly designed, the observer driven by the disturbance estimator is convergent; and that all subsystems in the closed-loop are asymptotically stable. Some numerical simulations are provided.

Keywords: Observers for linear systems, Linear systems, Distributed parameter systems
Abstract: We consider the problem of recovering the initial data (or initial state) of infinite-dimensional linear systems generated by a perturbed skew-adjoint operator. It is well-known that this inverse problem is well posed if the system is exactly observable, but this assumption may be very restrictive in some applications. In this paper we are interested in the slight generalization of the results by Haine (MCSS, 2014) in the case where the generator is a simple perturbation of a skew-adjoint operator. The reconstruction algorithm is based on iterative forward and backward observers, using the algorithm of Ramdani, Tucsnak and Weiss (Automatica, 2010).

Keywords: Distributed parameter systems, Identification, Variational methods
Abstract: This article deals with the problem of estimating the empirical parameters in a traffic flow modeled by the so-called Lighthill-Whitham-Richards equation. The optimal values of parameters are estimated by solving the inverse problem of minimizing the least square error between simulations and lumped observation values. The system equation and adjoint model equation, obtained by using adjoint method, are discretized and solved via the nonlinear implicit Preissmann scheme. The parameters to be estimated are then obtained through an optimization process containing steepest descent method and backtracking line search method. A numerical simulation is finally given in order to illustrate the effectiveness of here-proposed approach.

Keywords: Distributed parameter systems, Optimal control
Abstract: The aim of this paper is to study the approximation of the solutions of time optimal control problems for a class of infinite dimensional linear systems by projecting the original problem on an appropriate family of finite dimensional spaces.

Keywords: Distributed parameter systems, Observers for linear systems, Adaptive systems
Abstract: We are considering the problem of state observation for a class of infinite dimensional systems. The latter are modelled by a parabolic PDE and a boundary condition involving an unknown parameter. The problem is dealt with using an adaptive observer that provides online estimates of the system (spatially distributed) state and the unknown parameters. The observer is formally shown to be exponentially convergent under an ad-hoc persistent excitation condition.

Keywords: Identification, Distributed parameter systems, Applications in neuroscience
Abstract: This paper discusses the estimation of distributed Cerebral Blood Flow (CBF) using spatiotemporal traveling wave model. We consider a damped wave partial differential equation that describes a physiological relationship between the blood mass density and the CBF. The spatiotemporal model is reduced to a finite dimensional system using a cubic b-spline continuous Galerkin method. A Kalman Filter with Unknown Inputs without Direct Feedthrough (KF-UI-WDF) is applied on the obtained reduced differential model to estimate the source term which is the CBF scaled by a factor. Numerical results showing the performances of the adopted estimator are provided.

Keywords: Aerospace, Predictive control for linear systems, Autonomous systems
Abstract: This tutorial paper provides a review of recent advances in the field of spacecraft rendezvous using model predictive control (MPC), an advanced optimal control strategy based on on-line constrained optimisation of control inputs based on predictions of future trajectories. Firstly, the rendezvous objectives, and the generic constrained MPC problem formulation are summarised. This is followed by a discussion of how to select the three key ingredients used in an MPC design: the prediction model, the constraints and the cost function. Since MPC implementation relies on finding the solution to constrained optimisation problems in real-time, computational aspects are also briefly examined. The paper concludes with conjecture on ways the use of MPC in this application could be further advanced.

Keywords: Aerospace, Predictive control for nonlinear systems, UAV's
Abstract: This tutorial paper describes a high-level guidance algorithm based on model predictive control. The algorithm follows a precomputed flight plan (which provides a reference trajectory based on straight segments) by calculating the aerodynamic velocity, flight path angle and bank angle commands for a low level attitude controller. To compute these control signals, the guidance law uses a nonlinear 3DoF aircraft model for state prediction that leads to a nonlinear optimization problem, which is solved using a iterative scheme. The algorithm uses as a hotstart a robust missile guidance law, thus guaranteeing feasibility. Simulation results show the effectiveness of the proposed guidance law.

Keywords: Aerospace, Predictive control for linear systems, Predictive control for nonlinear systems
Abstract: While electric propulsion generates fuel-efficient thrust for spacecraft control, it can only produce low levels of thrust, necessitating continuous actuation to impart an equivalent impulse to that of chemical thrusters. Thus, many of the standard open-loop propulsion scheduling techniques, developed for impulsive thrust, do not transfer to low-thrust architectures. Continuous actuation, together with tighter anticipated requirements for spacecraft station keeping, e.g., in geostationary Earth orbit (GEO), provides great opportunity for the application of feedback control. We demonstrate that model predictive control (MPC) can provide significant advantages as a control strategy for station keeping of low-thrust spacecraft, provided that its features, such as the capability to enforce output constraints, and the use of a prediction model for the plant and disturbances, are fully exploited. We develop a basic MPC design for station keeping in GEO, and compare its performance with an advanced MPC design that includes output constraints and disturbance prediction. We show that the basic MPC achieves precise regulation, albeit with unsustainable fuel consumption, whereas the advanced MPC satisfies the target mission requirements with fuel consumption in line with that of carefully designed open-loop strategies.

Keywords: Aerospace, Hybrid systems, Predictive control for linear systems
Abstract: In this tutorial paper we present a novel modeling methodology to derive a nonlinear dynamical model which adequately describes the effect of fuel sloshing on the attitude dynamics of a spacecraft. We model the impulsive thrusters using mixed logic and dynamics leading to a hybrid formulation. We design a hybrid model predictive control scheme for the attitude control of a launcher during its long coasting period, aiming at minimising the actuation count of the thrusters.

Keywords: Aerospace, Predictive control for nonlinear systems, Optimal control
Abstract: In this paper we propose a Model Predictive Controller for spacecraft attitude tracking with reaction wheel actuators. The controller is designed for desaturation of the reaction wheels. In contrast with standard desaturation techniques, which rely on the activation of thrusters, the proposed strategy does not need to consume fuel as it exploits external torques derived from gravity gradients and/or the Earth magnetic field. The controller also guarantees that the spacecraft attitude is constrained within specified bounds during desaturation.

Keywords: Aerospace, Predictive control for linear systems, V&V of control algorithms
Abstract: Future safety critical space missions call for increasing levels of embedded spacecraft autonomy. Spacecraft and mission responsiveness will be highly improved via on-board automation and autonomy functions, simplifying operations and ground control capabilities. Recently developed real-time embedded MPC guidance and control strategies have a great potential for the next generation of high performance reusable ESA launch vehicles and GNC systems. This paper addresses these technologies for real-time embedded MPC covering thrust vectored control that could be proficiently used during the ascent phase as well as on powered descent enabling accurate pin-point landing features.

Keywords: Iterative learning control, Adaptive control
Abstract: This paper proposes a control algorithm for a Toyota gasoline engine problem that is addressed in a student competition format. The control objective is to minimize the fuel consumption while avoiding specified dangerous situations. The approach develops a feed-forward control based on an adaptive Iterative Learning Control. In this method, the plant is run several times and the controller iteratively updates the actuation inputs in order to generate the desired reference torque profile. The algorithm converges after approximately 10 iterations providing the corresponding locally optimal control trajectories.

Keywords: Automotive
Abstract: In this paper, an improved PI classical controller type is designed to reduce produced emissions in spark ignition engines. To this end, by pushing engine in its safe operational condition, fewer fuel consumption with reasonable torque error are acquired. Afterwards, in order to enhance the presented controller efficiency, the parallel optimization of control parameters are obtained with genetic algorithm since the system inputs are coupled with each other. So that, the maximum achievable engine performance are gathered by presented approach. Eventually, simulation results illustrate effectiveness of proposed scheme in comparison with provided baseline controller method.

Keywords: Automotive, Optimization, Predictive control for linear systems
Abstract: In this paper, the torque control problem for spark-ignition engines is considered. The objective is to provide good output torque tracking with minimum fuel consumption, while avoiding engine knock and misfire. To this end, three control strategies are proposed: a feed-forward controller with recalibrated engine maps, a PI control scheme and a model predictive controller. For the model predictive control strategy, a simplified air-path engine model is developed based on the Wiener model structure. The proposed controllers are shown to provide safe engine operation by avoiding engine knock and misfire. The performance of each control strategy is evaluated over a specific driving cycle and compared with a baseline control strategy. Simulation results show up to 40% performance improvement for the model predictive control strategy as compared to the baseline strategy.

Keywords: Adaptive control, Predictive control for nonlinear systems, Automotive
Abstract: This paper deals with the optimal tracking control of a spark ignited combustion engine. The overall controller consists of an (a priori given) feed-forward part that steers the system close to the desired trajectory and an optimal adaptive predictive controller for the tracking error dynamics. The latter is based on estimating a linear model along the desired trajectory online, which is then used in a Model Predictive Control (MPC) scheme that systematically incorporates the input and state constraints in the design process. The provided feed-forward control and its respective combination with an infinite Linear-Quadratic Regulator (LQR) and the adaptive MPC are compared in simulation. The results demonstrate a decrease in fuel consumption at the price of a minor performance loss in terms of tracking the desired torque cycle, while avoiding disadvantageous operation of the engine at all times.

Keywords: Automotive, Predictive control for nonlinear systems, Modeling
Abstract: With the goal of minimizing fuel consumption and torque tracking error, while also avoiding knock and misfire, a traditional engine controller may be conservative since it does not consider transient behavior. In this paper, an approximate nonlinear model predictive control (NMPC) is presented for use on a gasoline engine with exhaust gas recirculation (EGR). In the NMPC framework, a nonlinear dynamic model of the engine is used to train a state feedback controller, while also considering the constraints. The resulting controller is implemented as look-up tables that are fast to compute in real time. An unscented Kalman filter is used for state estimation. Testing on a benchmark engine simulator shows a significant performance improvement over the baseline controller.

Keywords: Uncertain systems
Abstract: Control of emissions in gasoline engines has become more stringent in the last decades, especially in Europe. This poses new and important problems in the control of complex nonlinear systems. In this work a preliminary investigation is conducted on the idea of exploiting Iterative Learning Control to optimize input--output maps that are commonly used in Engine Control Units of gasoline engines. In this spirit, starting from existing maps, we show how to refine them using a gradient--descent iterative learning control algorithm, considering additional constraints on the optimization problem. The result of this process is a control signal which can be integrated in a modified map. The performances of this process are validated on a training signal and cross--validated on different torque reference signals. Simulation results show that the approach is promising.

Keywords: Automotive
Abstract: Herein, addressing the optimal control problem of a simulated gasoline engine is aimed. The challenge demands tracking a desired torque profile with the minimum possible fuel consumption while promising no knock and misfire in all cylinders. The present study suggests a solution by means of Proportional-Integral-Plus (PIP) control algorithm. In this way, a MIMO model is extracted for the system using refined instrumental variable, which is known as a linear identification method. An LQ optimal design is used to obtain control gain matrix for PIP control formulation. Controller hyper parameters are then optimized through a constrained nonlinear evolutionary search. The constraint function from the plant ensures avoidance of the two mentioned threatening situations for the engine. Final results show that, in comparison to the controller tuned manually, the optimized system consumes lower amount of fuel beside a better tracking performance of the desired input and no reported constraint violation all during the simulation course.

Keywords: Hybrid systems
Abstract: The problem of finding a finite state symbolic model which is bisimilar to a hybrid dynamical system (HDS) and has the minimum number of states is considered. The solution to this problem may fail to exist and the conditions for existence of solution are not completely known. In this paper, we propose a computational procedure for this problem. The procedure is based on computing the behaviors up to a finite length for a finite set of samples from the state space. No time discretization is applied. No stability assumptions have been made. The requirements for the success of the proposed procedure are the existence of a bisimulation and taking an enough number of samples from the state space. Although this number is not known currently, the result is still useful due to the difficult nature of the problem. It is possible to increase the confidence in correctness of the solution by iterating the procedure. The results are applied to an example HDS.

Keywords: Hybrid systems, Algebraic/geometric methods, Output regulation
Abstract: This work deals with output regulation for a class of multivariable hybrid dynamical systems: namely, those characterized by a continuous-time linear time-invariant dynamics, subject to periodic state jumps. More specifically, given a hybrid plant and a hybrid exogenous system generating a reference signal, the problem consists in finding a feedback hybrid regulator that achieves asymptotic tracking of the reference signal and global asymptotic stability of the closed-loop dynamics. Necessary and sufficient conditions for the existence of a solution are derived, on the basis of the geometric properties of the given plant and of the exogenous system. The if-part of the proof shows how to synthesize the feedback regulator under those conditions. A numerical example is worked out with the aim of illustrating how to apply the devised methodology.

Keywords: Hybrid systems, Control over networks, Lyapunov methods
Abstract: This article discusses ultimate boundedness and stochastic stability of event triggered green control for discrete time dynamical systems. Event triggered green control or hands-off control reduces control efforts by turning off the actuator with help of Event Triggering Mechanism (ETM). Our analysis is based on a Lyapunov based framework. Also, a novel ETM is proposed which is non-trivial and equipped with the property of hands-off.

Keywords: Hybrid systems, Delay systems, Observers for nonlinear systems
Abstract: The paper deals with the model-based estimation of hormone concentrations that are unaccessible for direct measurement in the blood stream. Previous research demonstrated that the dynamics of non-basal endocrine regulation can be closely captured by linear continuous models with time delays under a pulse-modulated feedback. The presence of continuous time delays is inevitable in such a model due to transport phenomena and the time necessary for an endocrine gland to produce a certain hormone quantity. Yet, thanks to the Finite-Dimensional reducibility of the linear time-delay part of the system, a finite-dimensional model can be used to reconstruct both the continuous and discrete states of the hybrid time-delay plant. A hybrid observer exploiting this possibility is suggested and analyzed by means of a discrete impulse-to-impulse mapping.

Keywords: Hybrid systems, Distributed cooperative control over networks, Lyapunov methods
Abstract: The objective is to design distributed coordination strategies for a network of agents in a cyber-physical environment. In particular, we concentrate on the rendez-vous of agents having double-integrator dynamics with the addition of a damping term in the velocity dynamics. We start with distributed controllers that solve the problem in continuous-time, and we then explain how to implement these using event-based sampling. The idea is to define a triggering rule per edge using a clock variable which only depends on the local variables. The triggering laws are designed to compensate for the perturbative term introduced by the sampling, a technique that reminds of Lyapunov-based control redesign. We first present an event-triggered solution which requires continuous measurement of the relative position and we then explain how to convert it to a self-triggered policy. The latter only requires the measurements of the relative position and velocity at the last transmission instants, which is useful to reduce both the communication and the computation costs. The strategies guarantee the existence of a uniform minimum amount of times between any two edge events. The analysis is carried out using an invariance principle for hybrid systems.

Keywords: Hybrid systems, Mechatronics, Robotics
Abstract: This paper proposed a structurally novel ``hybrid manipulator", which serially connects a binary manipulator with a Gough-Stewart type parallel manipulator. Both structures are known as high rigidity and stability for static loads. The binary manipulator has wide workspace, however, its workspace is discrete. On the other hand, the parallel manipulator has high accuracy for continuous positioning, however, its workspace is narrow. Therefore, the proposed structure possesses advantages of both the binary and parallel manipulators.

This paper also proposes an ellipsoidal outer approximation of the manipulator's workspace. This approximation can be utilized in an inverse kinematics algorithm. Some numerical experiments will show the validity of the proposed approximation algorithm.

Keywords: Distributed control, Large-scale systems, Energy systems
Abstract: This paper deals with the computational efficiency evaluation of a hierarchical DMPC (distributed model predictive control) framework for resource sharing problems which has been established in the context of smart district energy management. The provided DMPC framework is based on a dual decomposition of the centralized open-loop controller which is decomposed into several subproblems and one coordinator problem. At coordinator level the bundle method is used in order to recover the globally optimal solution through an iterative process. The main focus of this paper is a detailed discussion of the impact of the bundle method’s parametrization on the computational performance of the whole scheme. Additionally a qualitative comparison with a similar scheme based on primal decomposition is provided and some rules of thumb for determining an effective parametrization of the bundle method are established. In the provided simulations the scheme is applied to a large-scale problem of the smart district context. More precisely the centralized optimization problem of a district composed of 1000 buildings sharing a globally limited power resource can be solved to optimality using our proposed framework in around 3 seconds.

Keywords: Distributed control, Optimization, Optimal control
Abstract: This paper studies the optimal supply levels of Power-to-Gas (PtG) facilities equipped with local storage devices and fuel cells. The produced energy can be directly injected in the gas grid, utilized in chemical/mobility industry, and stored in a storage device and later on reconverted back into electrical energy using fuel cells before injecting it to the power grid. The aim of the control process is to maximize the PtG facilities' profit from the produced energy while avoiding overloading grids. The problem is solved by a distributed model predictive control (MPC) approach in which each PtG facility makes decisions based on its local information, yet still coordinates its supply bids to the grid operators. In this way, grid operators can manage PtG facilities via prices associated with the grid constraints in order to avoid overloading. We perform simulations to investigate the evolution of prices modified by grid operators and to study the economic feasibility of PtG facilities under realistic estimates of the price and demand patterns on the energy grids.

Keywords: Distributed control, Robust control, Traffic control
Abstract: In this paper, we develop a distributed state-feedback controller synthesis algorithm for a discrete-time LPV system that is composed of the interconnection of several subsystems each scheduled by its own parameters. A set of LMI conditions are derived for robust L2-gain performance of such a system in the framework of multiplier-based LPV synthesis. The results have been oriented to be applied in traffic flow control in motorways by ramp metering. First, with the use of a proper transformation, the nonlinear traffic flow model has been represented as the interconnection of LPV subsystems. Then the developed synthesis results have been used to design a gain-scheduled distributed state-feedback controller that keeps the density of all segments around a desired value by the use of ramp metering.

Keywords: Distributed control, Optimization algorithms
Abstract: The purpose of this paper is to propose a protocol for distributed multi-agent optimization problem to minimize the average of objective functions of the agents in the network with satisfying constraints of each agent. The exact penalty method is adopted to distributed optimization via a linear protocol, with employing two step-size parameters for the objective function and the constraint function of each agent. A proof of the convergence of the proposed protocol is provided as well as the boundedness under mild assumptions. The protocol is also illustrated by a numerical example.

Keywords: Distributed cooperative control over networks, Distributed control, Linear systems
Abstract: This paper presents necessary and sufficient conditions which guarantee consensus for a homogeneous network of linear second-order discrete-time systems. The eigenvalues of the graph Laplacian can be complex. Linear controllers which achieve consensus with a guaranteed rate of convergence are presented. The paper concludes with an example where the proposed controllers synchronize a network of discrete-time double integrators with several different convergence rates.

Keywords: Distributed control, Optimization algorithms, Chemical process control
Abstract: The efficient real-time plant-wide coordination of petrochemical production sites is a challenging and yet not fully resolved problem and its solution can substantially improve the productivity and the efficiency of such complex petrochemical plants. While a site-wide integrated optimization might be feasible in principle, the size of the problem, the unavailability of data and the management structures of the plants favour a distributed solution, where the global coordination has to be achieved by suitable mechanisms. In this paper we propose price based coordination in a moving horizon setting to achieve plant-wide real-time coordination of the utilization of shared resources without communication of detailed knowledge of the structure of the subsystems. The shared resources are considered as market commodities that are traded between the units and global coordination is achieved by real-time adjustment of their market prices. We provide results of different price coordination methods for a model of an integrated petrochemical site. The results clearly show that market mechanisms can be applied for the coordination of large sites in the petrochemical industry.

Keywords: Energy systems, Emerging control applications, Optimization
Abstract: In this paper we deal with the problem of selecting an optimal set of magnetic sensors for the reconstruction of the plasma current density moments in a tokamak machine. Correct identification of the plasma current density moments is equivalent to a correct identification of the parameters which are important from a magnetic control point of view. For instance the 0-th order moment is the plasma current, whilst the 1-st and 2-nd moments give the vertical and radial position of the plasma current centroid. The choice of the best magnetic sensors is made basing on the solution of a suitable convex optimization problem. The optimal solution is tested in simulation evaluating the performance degradation in terms of estimation of the current density moments due to the reduced number of sensors. The numerical results are obtained in the presence of noise, with reference to the ITER tokamak.

Keywords: Energy systems, Lyapunov methods, Emerging control applications
Abstract: We study the control of an Airborne Wind Energy (AWE) system where a turbine is mounted on a rigid glider (or kite) with a fixed length tether connected between the kite center of gravity (CG) and the ground. The turbine on the kite in an AWE system extracts wind power in a high speed cross wind motion to efficiently harness energy. The model incorporates the full aircraft and inverted pendulum dynamics and results in an under-actuated and coupled system. In this paper, we discuss translation related velocity angles, which can be used to link the translational and rotational motion. A Lyapunov based nonlinear controller is designed to achieve a desired kite attitude trajectory for tracking a constant angle of attack. A baseline simulation of several successive power generation figure-eight cycles is presented.

Keywords: Energy systems, Optimization algorithms, Decentralized control
Abstract: Wind turbines working close to other turbines experience interactions that affect the power production. These interactions arise a consequence of wakes caused by upstream wind turbines. In order to achieve a more effective and precise control of the power generated by wind farms, the control strategy must consider these interactions. However, the phenomena involved in wake effects are complex especially in cases of large number of turbines. This paper presents the implementation of a gradient estimation-based algorithm as a model-free control for two different control schemes aimed to maximize the energy capture of a wind farm. One control is centralized leaving to a supervisor the task of command computation and the other is decentralized distributing the performing generation among wind turbines. This latter scheme aims to increase the reliability of the wind farm operation by reducing the communications needed to fulfil the objective of maximizing energy capture. Both control schemes are evaluated by simulation in the case of three-turbine wind farm.

Keywords: Energy systems, Observers for nonlinear systems
Abstract: This work presents a modeling and estimation techniques for State of Charge and State of Health estimation for Li-ion batteries. The analysis is done using an adaptive estimation approach for joint state and parameter estimation and by simplifying an existing nonlinear model previously obtained from experiments tests. A switching mechanism between two observers, one for the charging phase and one for the discharging phase, is done to avoid transients due to the discontinuity of model’s parameters. Simulations on experimental data show that the approach is feasible and enhance the interest of the proposed estimation technique.

Keywords: Energy systems, Predictive control for linear systems, Fuzzy systems
Abstract: This paper proposes a new design of a fuzzy predictive controller for collective pitching of large wind turbines. Collective pitch controllers operate in region three to harvest the rated power and maintain the rated speed. The wind turbine model is represented by a Takagi-Sugeno (T-S) fuzzy model. A model predictive collective pitch controller is designed based on the fuzzy model taking into consideration the pitch actuator limits and rates. The proposed controller is coupled with the conventional baseline PI controllers for individual pitch control so as to minimize the moment on the turbine blades. An extended Kalman observer is designed to estimate the immeasurable states. The performance of the proposed fuzzy-predictive controller is compared with a conventional PI controller. Simulation results based on a typical 5-MW offshore wind turbine demonstrate the superiority of the proposed fuzzy-predictive controller.

Keywords: Energy systems, Predictive control for linear systems, Computer aided control design
Abstract: Model predictive control has in recently publications shown its potential for lowering of cost of energy of modern wind turbines. Pareto curves can be used to evaluate performance of these controllers with multiple conflicting objectives of power and fatigue loads. In this paper an approach to update an model predictive wind turbine controller tuning as the wind turbulence increases, as increased turbulence levels results in higher loads for the same controller tuning. In this paper the Pareto curves are computed using an industrial high fidelity aero-elastic model. Simulations show the potential in this approach, how a weight matrix selection based on an estimated wind turbulence, can keep the relevant Damage Equivalent Loads at the same level in a high turbulence case as in the normal turbulence case.

Keywords: Biological systems, Fault detection and identification, Emerging control applications
Abstract: Using measurements of valve activity in a population of bivalves under natural environmental condition (16 oysters in the Bay of Arcachon, France), an algorithm for the automatic detection of spawning period of oysters is proposed. The algorithm is based on the fault detection approach and it works through the estimation of velocity of valve movement activity, which can be obtained by calculating the time derivative of the valve distance activity. A summarized description on the method used for the derivative estimation is provided, followed by the associated signal processing and decision making algorithm to determine spawning from the velocity signal. A protection from false spawning detection is also considered by analyzing the synchronicity in spawning. Through this study, it is shown that spawning in a population of oysters living in their natural habitat(i.e. in the sea) can be automatically detected without any human expertise, like visual analysis

Keywords: Biological systems, Control over networks, Concensus control and estimation
Abstract: In this paper, we revisit the linear continuous time distributed consensus problem and provide a simple and elegant proof under very mild assumptions. Our approach is based on a novel extension of the contraction coefficient that can be adapted to the continuous time version of the model. We apply our results in non-linear second order consensus networks of Cucker-Smale type and we obtain new initial conditions for asymptotic flocking.

Keywords: Biological systems, Nonlinear system theory, Lyapunov methods
Abstract: This paper proposes a new system model called a positive quadratic system for molecular interaction in a cell, including a signal transduction pathway and a gene regulatory pathway. Although the whole network of these pathways is far more complex than simple chemical reactions, one of the features of the proposed model is that it can capture the quadratic property of molecular interaction as well as the positivity properties of the state variables with a rather simple form. Based on this model, we derive a sufficient condition of the stability in the sense of Lyapunov, and present a method for estimating a positive invariant set depending on the initial state. We show that the proposed method is effective by numerical simulations of two fundamental biological models.

Keywords: Biological systems, Identification, Modeling
Abstract: Recent advances in measurement technology provide us with rich source of data for estimating parameters in biomolecular circuit models, particularly in simplified in vitro transcription-translation systems, so-called molecular ``breadboards". In this paper, we elaborate on a mass action type dynamic model for such an in vitro system and detail a parameter estimation procedure that may be used with time series data containing information about both transcriptional and translational stages of gene expression. The identification process is supported by structural identifiability analysis to ensure proper model structure. Statistical analysis and validation of the estimated parameter set help us to understand the characteristics of point estimation results.

Keywords: Biological systems, Uncertain systems, Cellular dynamics
Abstract: In this paper we extend previous attempt to explore the possible robustness of the Threonine Synthesis pathway in the H_infty and the L_infty -L_infty senses. Following linearization of the pathway kinetic equations, two modelling strategies are applied the first of which tests the robustness of the nominal linearized system to various parameter uncertainties, including uncertainties in the control gain matrix of the system. In the second strategy similar tests of the latter are applied however, the original nonlinear system is modelled as an uncertain liner system where the extent of nonlinearity is tuned by the uncertainty interval. It is shown that the system sensitivity to the various uncertain parameters agree in both strategies and that the two system performance indices, the H_infty and the L_infty -L_infty better represents the system behavior compared to experimental results that were obtained for the Threonine Synthesis pathway.

Keywords: Biological systems, Feedback linearization, Nonlinear system theory
Abstract: Using the well known approach of feedback linearization, we study problems that can be applied to controlling the rotational motion of a pair of human eyes. Eyes move to acquire a fixed point target and the control task is to direct the eye-pair towards a general target direction and, if the target is close by, to focus on the target. Roughly speaking, the former task is accomplished by versional eye movements and the latter task of pinpointing the eye on a specific target is accomplished by vergence eye movements. In this paper, we separately synthesize asymptotically stabilizing controllers, in the neighborhood of a target point, for version and vergence control systems. This is accomplished by defining a suitable output and regulating the output to the origin, while the eyes gaze towards the target.

Keywords: Robust control, Optimal control, Chemical process control
Abstract: Dynamic optimization techniques for nonlinear systems can provide the process industry with sustainable and efficient operating regimes. However, these regimes often lie close to the operating limits. It is therefore critical that these model based operating conditions are robust with respect to process noise, i.e, unmodeled time-varying random disturbances. Besides the effect of uncertainty in the satisfaction of constraints, also the effect of uncertainty on the objective function should be considered. Including uncertainty in an optimization problem typically leads to numerically challenging semi-infinite optimization problems. In this paper several computationally tractable methods are exploited to approximately solve robust optimal control problems. The presented approaches have the advantage that they allow the use of fast deterministic gradient based optimization techniques. The first method is based on a linearization approach while the second method exploits the unscented transformation to construct an estimate of the uncertainty propagation. Both methods yield an approximation of the variance-covariance matrix of the critical constraints and of the objective function. These variance-covariance matrices are employed in the optimization routine to obtain more robust control actions. The illustrative case study concerns a jacketed tubular reactor.

Keywords: Predictive control for linear systems, Control over communication, Robust control
Abstract: In this paper we present a robust self-triggered model predictive control (MPC) scheme for discrete-time linear time-invariant systems subject to input and state constraints and additive disturbances. In self-triggered model predictive control, at every sampling instant an optimization problem based on the current state of the system is solved in order to determine the input applied to the system until the next sampling instant, as well as the next sampling instant itself. This leads to inter-sampling times that depend on the trajectory of the system. By maximizing the inter-sampling time, the amount of communication in the control system is reduced. In order to guarantee robust constraint satisfaction, Tube MPC methods are employed. Specifically, in order to account for the uncertainty in the system, homothetic sets are used in the prediction of the future evolution of the system. The proposed controller is shown to stabilize a closed and bounded set including the origin in its interior.

Keywords: Emerging control applications, Optimal control, Uncertain systems
Abstract: This paper applies a novel two-layer optimizing control scheme to a kite-control benchmark problem. The upper layer is a recent real-time optimization algorithm, called Directional Modifier Adaptation, which represents a variation of the popular Modifier Adaptation algorithm. The lower layer consists of a path-following controller that can follow arbitrary paths. Application to a challenging benchmark scenario in simulation shows that this two-layer scheme is capable of substantially improving the performance of a complex system affected by significant stochastic disturbances, measurement noise and plant-model mismatch, while respecting operational constraints.

Keywords: Predictive control for nonlinear systems, Robust control, Process control
Abstract: In this paper we present an approach to deal with structural uncertainties (structure and dimension of the model are not perfectly known) in the framework of nonlinear model predictive control (NMPC). The presented method is based on robust multi-stage NMPC which represents the uncertainty as a scenario tree with the possibility of recourse, reducing the conservativeness of the approach. In the case of structural mismatch, the unmodeled dynamics can cause a significant difference between the model used for the predictions and the reality, which can cause violations of constraints. We generate a scenario tree to account for the mismatch between the plant and the model by assuming a bound on the effect of the structural mismatch on the model. Thus the proposed scheme will be robust to the structural mismatch present in the model. We demonstrate the advantages of the proposed approach for a nonlinear continuous stirred tank reactor example.

Keywords: Optimization algorithms, Uncertain systems, Robust control
Abstract: In the field of nonlinear model predictive control (NMPC) under uncertainty we use a robustification approach based on a scenario tree formulation. This approach is known to be less conservative than worst-case approaches. A main challenge of scenario tree NMPC in high-dimensional uncertainty spaces is the exponential growth of the number of scenarios for possible parameter realizations. Hence, the solution of the resulting optimization problem in every NMPC iteration becomes a bottleneck for the computation. We have to solve the problem with fast numerical methods to ensure real-time applicability of scenario tree NMPC. By a multiple shooting discretization we discretize the optimal control problems of an NMPC iteration to obtain a sequence of Nonlinear Programs that we solve by a real-time feasible variant of Sequential Quadratic Programming (SQP). Every single Quadratic Programming problem (QP) exhibits a particular structure originating from the scenario tree. We use a dual decomposition approach on the non-anticipativity constraints from the scenario tree formulation to decouple the large-scale QP into many smaller QPs that each correspond to one scenario in the scenario tree. Within an outer level non-smooth Newton iteration in the dual space of the coupling constraints, the decoupled scenario QPs can be solved in a massively parallel fashion. In the final part of this contribution we present numerical results for NMPC with large scenario trees.

Keywords: Nonlinear system theory, Optimization algorithms, Electrical machine control
Abstract: A novel approach for the active surge control of centrifugal compression systems with actuated recycle valves is presented. This actuator, commonly used in industrial plants, allows the system to recover from surge by increasing the mass flow rate through the compressor. A backstepping control is designed for the recycle valve, and is combined with a nonlinear Model Predictive Control of the drive torque, to track a desired reference state while accounting for system constraints. The optimization problem is solved approximately, to account for the computational limitation of industrial controllers. Simulations based on a real compression system show that the proposed control system tracks a desired reference and is robust to uncertainties in the outlet valve characteristics.

Keywords: UAV's, Robotics, Feedback linearization
Abstract: Given the tethered aerial robot problem, in this paper we nvestigate a multi-agent extension considering a chain of two underactuated flying robots. Our goal is to independently control the elevations (angles) of the two links (or, quivalently, the Cartesian position of the last robot) and their internal stress. For this purpose we theoretically rove the dynamic feedback linearizability of the system and we exploit this property to design a nonlinear controller that exactly linearizes the system. The controller is able to steer the outputs of interest along any trajectory s.t. the desired elevations and stresses are functions of time of class C³ and C¹ , respectively. The controller is able to track independently both positive and negative stresses, i.e., both tension and compression. Resorting to an equivalence argument we then also show the differential flatness of the system. Finally we also demonstrate some of the abilities of the proposed method through numerical examples.

Keywords: UAV's, Communication networks, Optimization algorithms
Abstract: We discuss a simplified version of an open problem in system realization theory which has several important practical applications in complex networks. Particularly, we focus on algebraic connectivity (lambda_2) of the Laplacian of the network as an objective for maximization. We show that the maximum value of forced response of a linear mechanical (spring-mass) system can be minimized when the lambda_2 of the corresponding stiffness matrix is maximized. In the case of motion control problems related to vehicle localization with noisy measurements, we show that the lambda_2 plays a vital role to control their positions towards a desired formation. In UAV adhoc infrastructure networks, we show that the lambda_2 of the information flow graph governs the stability of the rigid formation with respect to disturbance attenuation. In the context of UAV adhoc communication networks, we also provide a physical interpretation for the maximization of lambda_2 via the closely related Cheeger constant or the isoperimetric number. We further discuss a Fiedler vector based mixed-integer linear programing formulation for the problem of maximizing lambda_2 and obtain optimal solutions and upper bounds based on cutting plane methods. Computational results corroborate the performance of proposed algorithms.

Keywords: UAV's, Switched systems, Cooperative control
Abstract: This paper presents a shared-control scheme for a UAV moving in a 3D space while its feasible Cartesian position set is defined by a group of linear inequalities. A hysteresis switch is used to combine the human input and the feedback control input based on the definitions of a safe set, a hysteresis set and a “dangerous” set. Case studies given in the paper show the effectiveness of the shared-control algorithm.

Keywords: UAV's, Fault diagnosis, Observers for nonlinear systems
Abstract: This paper presents a fault diagnosis strategy for actuators faults in an octorotor unmanned aerial vehicle (UAV) using sliding modes techniques. First, system states are estimated using a second order sliding-mode observer (SOSMO) based on the modified super-twisting algorithm. Second, faults are estimated by considering the system control as an unknown input. After the convergence of the observer, the equivalent output injection is used to estimate the unknown input and thus to identify losses in the actuators. The effectiveness of this approach is illustrated by numerical simulations on Matlab/Simulink, and also a real experimental application on a coaxial octorotor UAV.

Keywords: UAV's, Autonomous systems
Abstract: In this paper a semi-analytical technique for generating quadrotor trajectories using the projection of sub-Riemannian curves on SE(3) onto three-dimensional space is presented. The benefits of using these trajectories are that they have constant speed and acceleration magnitude, are smooth and analytically defined in terms of standard functions, kinematically feasible and optimal with respect to a weighted quadratic cost function of the translational and angular velocities. This paper shows, through simulation, that these curves are also dynamically feasible. In addition, a method for changing the velocity profile along these curves using time parametrisation, allowing for smooth rest-to-rest manoeuvres, is shown. An obstacle avoidance strategy using these analytically defined curves is demonstrated by producing multiple trajectories of different shapes. This is achieved by adding a final velocity perturbation to the cost function that is minimised when generating the trajectory.

Keywords: UAV's, Adaptive systems, Fault estimation
Abstract: The accretion of ice layers on wings and control surfaces modifies the shape of the aircraft and, consequently, alters performance and controllability of the vehicle. In this paper we propose a multiple model adaptive estimation framework to detect icing affecting unmanned aerial vehicles using the following sensors: pitot tube (airspeed sensor), GPS and IMU. A case-study is provided as support and validation of theoretical results.

Keywords: Quantized systems, Control over communication, LMI's/BMI's/SOS's
Abstract: This paper considers a sampled-data model following control for continuous-time discrete-valued input systems. A synthesis condition is recast as a set of matrix inequalities via the matrix uncertainty approach, while the quantized accuracy and the switching speed of discretizing the continuous-valued signal are considered simultaneously. To reduce the conservativeness of the synthesis condition, the improved approach dividing the region of the switching intervals is also proposed.

Keywords: Quantized systems, Output feedback, LMI's/BMI's/SOS's
Abstract: This paper pertains to the design of a dynamic output-feedback controller for linear systems subject to both actuator and sensor quantization. The quantization errors affecting the measured output and the control input are embedded into compact sector through appropriate sector conditions. Then, the global asymptotic stabilization problem of an ellipsoidal set containing the origin is tackled. To deal with the discontinuity induced by quantization, the notion of Krasovskii solution is adopted to tackle the considered problem. The controller is designed via a convex optimization setup, providing a constructive method that can be efficiently implemented. Finally, the effectiveness of the proposed methodology is shown in a numerical example.

Keywords: Sensor and signal fusion, Filtering, Stochastic filtering
Abstract: In this paper, signal strengths from known WiFi access points are used together with a particle filter to perform indoor navigation. It is shown that more information is obtained by using signals of both 2.4 and 5.0 GHz, compared to using only one frequency. Thus, using both frequencies provides a more accurate positioning. The second contribution is an algorithm where WiFi measurements are combined with pedestrian dead reckoning (PDR), which is based on step counting using an accelerometer and hypotheses of the heading using a gyroscope. This was found to provide further accuracy compared to more conventional methods.

Keywords: Sensor and signal fusion, Observers for nonlinear systems, Filtering
Abstract: Range measurement systems are commonly based on measuring time-of-flight of signals encoded in electromagnetic or acoustic waves. This leads to so-called pseudo-range measurements due systematic errors such receiver clock synchronization error and uncertain wave propagation speed. Inertial navigation aided by pseudo-range measurements is addressed. A modular nonlinear observer is designed and analyzed. The attitude observer is based on a recent nonlinear complementary filter, and includes a gyro bias estimation. The translational motion observer includes the estimation of position, range bias errors (such as receiver clock bias), velocity and specific force, where the latter is used as a reference vector by the attitude observer. The exponential stability of the feedback interconnection of the two observers is analyzed and found to have a semiglobal region of attraction with respect to attitude observer initialization, and local region of attraction with respect to translational motion observer initialization. The latter is due to linearization of the pseudo-range and range-rate measurement equations that is underlying the selection of injection gains using a time varying Riccati equation. In typical applications the pseudo-range and range-rate equations admit an explicit algebraic solution that can be easily computed and used to accurately initialize the position and velocity estimates. Hence, the limited region of attraction is not seen as a limitation of the approach. Advantages of the proposed nonlinear observer include low computational complexity and a solid theoretical foundation.ation.

Keywords: Mechatronics, Servo control, Sensor and signal fusion
Abstract: An active vibration isolation system has been proposed which uses an active dynamic vibration absorber as a servo accelerometer in a low-frequency range and as a vibration control device in a high-frequency range. Complimentarily, it uses an air actuator as a control device in a low-frequency range and a low-cost MEMS accelerometer as a sensing device in a high-frequency range. The main aim of this research is to develop a high performance active vibration isolation system that is lower in cost than conventional active vibration system. It is experimentally demonstrated that the acceleration in a low frequency range is estimated from the control signal of the absorber and the vibration is actually attenuated by feeding back the estimated acceleration.

Keywords: Servo control, Mechatronics, Predictive control for linear systems
Abstract: The paper deals with an experimental approach to compensate a friction hysteresis effect in an aggregate actuator which is proposed to be applied in a camless engine system and which consists of piezoelectric, mechanic and hydraulic components. Piezo, mechanical and hydraulic friction effects generate a conspicuous hysteresis effect with a consequent final phase delay on the valve trajectory. The friction hysteresis effect can be seen as a cumulative friction phenomenon due to the cascade structure of the proposed actuator. The friction is modelled and simulated using a combination of Coulomb and viscous model but the compensation of the hysteresis effect is not model-based since the real time application must run within a very short time. Therefore the proposed approach is an experimental one. An experimental friction hysteresis compensator with a cascade controller combined with feedforwad actions is adopted to minimize friction effects and the resulting valve phase delay. This approach consists of an adaptive pre-action on the servo piston desired trajectory which is generated by a feed forward action fed by phase-variable Gaussian curves as desired valve trajectories. Experimental results obtained with a test stand prove the effectiveness of the proposed compensating techniques.

Keywords: Cooperative control, Autonomous systems, Distributed control
Abstract: This work considers a multi-agent formation control problem where a designated leader is subjected to an additional velocity reference command. The entire formation should follow the leader while maintaining the inter-agent distance constraints. By augmenting a standard gradient formation controller with a proportional-integral control on the formation error, we are able to prove the stability of the formation error dynamics with velocity input while ensuring zero steady-state formation error. Numerical simulations are shown to illustrate the theoretical results.

Keywords: Cooperative control, Cooperative autonomous systems, UAV's
Abstract: This paper extends our previous results on cooperative target tracking of a moving ground vehicle using multiple unmanned aerial vehicles with constant 2D horizontal range to a time-varying horizontal range. An early-designed guidance law that orbits one UAV around the target is modified to handle time-varying horizontal range given by a prescribed reference. Cooperative target tracking is achieved by combining two control efforts together, where one brings each UAV to orbit above the target and the other spreads all UAVs evenly on a circle. Three communication topologies are considered, i.e., an all-to-all communication, a ring topology, and a cyclic pursuit topology. The effectiveness of the proposed cooperative target tracking control laws is demonstrated by numerical simulations under the cyclic pursuit communication topology as an example. Key Words: Cooperative target tracking, balanced circular formation, time-varying formation radius, cyclic pursuit.

Keywords: Cooperative control, Game theoretical methods, Cooperative autonomous systems
Abstract: In this paper, we consider the problem of designing a resilient cooperative system from the perspective of differential game theory, as the conflict between a defender (i.e., the system’s control designer) and any potential attacker. Intuitively, a resilient system is one in which the defender is a superior player to any potential adversary. It is shown that, for cooperative networked systems that have a multi-layer information structure, the defender is superior to the attacker if the defender has a superior information structure in that it has access to more layers of information than those accessible to the attacker. This insight reveals that the networked consensus dynamics can be made resilient against attacks by introducing a hidden-layer network interconnected to the original consensus network but shielded from the attacker. Nash equilibrium and Lyapunov analysis are used to demonstrate resilience under all adversary strategies. Several scenarios are used to illustrate the proposed concepts and designs.

Keywords: Cooperative control, Optimal control, Game theoretical methods
Abstract: A collection of double integrator agents is considered, where each agent has bounded input and a limited range for receiving information from other agents. One autonomous node (the Root) generates an unknown reference trajectory to which other agents are required to converge in minimum possible time, using only locally available information. A distributed algorithm is proposed to identify a directed spanning tree rooted at the Root agent. The child agent of each edge of this selected tree uses min-max time control strategies to converge to the states of the parent node. Necessary and sufficient conditions are derived for limiting the inter-agent distance so as to preserve the tree communication structure chosen. Under these conditions, the proposed control strategy turns out to be the global communication preserving min-max strategy for the entire collection.

Keywords: Cooperative control, UAV's, Agents and autonomous systems
Abstract: Decentralised strategies for target tracking has been studied extensively in the literature. In this paper, we present a strategy which gives the flexibility to change formation while the UAVs are executing a mission. A target is assumed to be enclosed if the UAVs encircle the target at a given radius while being uniformly distributed around it. If we want to close on to the target or if we want to enclose a wider area around the target, the radius needs to change. In a decentralised scheme, all the vehicles have to be informed about the new radius, which is difficult when the number of vehicles are larger. The strategy proposed in this paper can induce a change in radius by changing a parameter in one of the vehicles picked randomly from the group. The idea is based on consensus in deviated pursuit. The control law is simple to compute and requires only bearing angle measurement. We have analysed the system assuming unicycle kinematics. The strategy is then implemented in 6-DOF dynamical model of the UAVs and simulated in MATLAB and in Hardware-in-loop simulator.

Keywords: Distributed cooperative control over networks
Abstract: In this paper we consider an event-triggered solution to the consensus problem of multi-agent systems. Our main contribution consists of the use of an integral based approach that is shown to reduce the conservatism intrinsic in the use of Lyapunov methods.

Keywords: Switched systems, Stability of hybrid systems, Lyapunov methods
Abstract: This paper concerns uniform global asymptotic stability (UGAS) for arbitrarily switched systems. A novel detectability condition is first proposed to guarantee a persistently exciting condition. The UGAS property can then be established for a class of nonlinear and time-varying switched systems that under the constraint of zeroing output have common (non-switching) limiting systems. A recently derived stability criterion is shown to be a special case of the proposed result. When applied to time-invariant systems, the well-known Krasovskii-LaSalle theorem can be deduced. Furthermore, we do not require dwell-time assumptions that are usually assumed in those results generalizing LaSalle invariance principle, thus providing more flexibility for the controller design. An interesting example is employed to demonstrate how the proposed result can be applied to guarantee the UGAS property without finding a common Lyapunov function that is normally not an easy job. This highlights the significance of the achieved results.

Keywords: Switched systems, Linear systems, Hybrid systems
Abstract: The paper addresses the controllability problem of switched linear systems. The time scales calculus is used to unify and extend existing results. The algebraic point of view on controllability problem is utilized in the paper. Necessary and sufficient conditions are derived. Illustrative examples are provided.

Keywords: Switched systems, Markov processes, H2/H-infinity methods
Abstract: This paper deals with dual switching linear systems in discrete-time. Such systems have piecewise linear dynamics, with switches dictated by two different sources. One is a stochastic switching signal modeled by a Markov chain. The other is a control discrete variable to be used for stabilization and disturbance attenuation. A first main result concerns the design of switching laws guaranteeing stability and H-infinity performance. We then proceed to tackle the more challenging co-design problem, namely the joint synthesis of a linear state-feedback controller and a stabilizing switching strategy.

Keywords: Switched systems, Optimal control, Optimization algorithms
Abstract: In this paper, an algorithm for switching time optimization for switched systems that exhibit discontinuities in the state trajectory on a switching is proposed. A derivative of the cost function with respect to the switching time is derived using a dynamic programming argumentation. Based on this derivative, a two-stage algorithm is described that alternates between solving an optimal control problem with fixed switching sequence and improving the switching times.

Keywords: Switched systems, Nonlinear system theory
Abstract: In this paper, we study the consistency property in the case of discrete-time switched Lur'e systems. More precisely the min-switching strategy and quadratic upper bounds of the cost function are considered. We propose an optimization problem that provides the smallest upper bound of the cost function which satisfies the consistency in this nonlinear case. The difference with the linear case is illustrated using an academical example. Then the result is applied to design a sampled-data controller with non uniform sampling. A numerical example is proposed to highlight the features of the proposed strategy.

Keywords: Identification, Stochastic systems, Signal processing
Abstract: In this paper, we propose a method to transform a non-positive real transfer function matrix into a positive real one. This problem is of engineering interest and arises when a linear time-invarant dynamics is identified by stochastic subspace identification methods. Recent methods to tackle this problem are based on semi-definite programming schemes and as illustrated by numerical examples in this paper suffer from leakage effect at peak frequencies of the modified frequency response. The method proposed in this paper is inspired from the matrix rank minimization problem, which consists of finding a matrix of minimum rank satisfying given convex constraints. This NP-hard problem is then solved by an iteratively reweighted nuclear norm heuristic. We apply this heuristic to the problem considered in this paper. Numerical examples show that this method converges only in a few iterations and is effective in eliminating leakages at peak frequencies.

Keywords: Identification, Uncertain systems, Optimization
Abstract: Traditional parameter estimation techniques deliver estimates for a given set of parameters, but do not in general provide an estimate of the parameter variability for systems with time-varying parameters. Accurate knowledge of the parameter variability becomes crucial in many contexts, e.g. robust control techniques. This paper proposes a method for joint parameter and variability estimation (PVE) which is based on optimizing a convex cost function subject to a linear matrix inequality (LMI) constraint. The method is described and compared to the Least Squares (LSQ) method for a linear AutoRegressive eXogenous (ARX) model with ellipsoidal parameter variability. The two techniques have been tested numerically in a simulation scenario. Simulation results indicate that PVE is more precise at characterizing the variability of estimated parameters.

Keywords: Identification
Abstract: In this paper, the problem of experiment design in single input single output (SISO) single carrier (SC) systems under a deterministic channel assumption is investigated and several connections with optimal preamble designs in multicarrier (MC) systems are established. In the context of SC systems, we derive optimal input sequences for the least-squares (LS) channel estimator under an input energy constraint. We then establish certain connections to optimal input designs for LS frequency domain channel estimation in MC systems. These connections reflect similarities between SC and MC systems both quantitative, i.e., in the achievable mean square error (MSE) performance, as well as qualitative, i.e., in the actual optimal input sequences. Simulations are provided to support the derived results.

Keywords: Identification
Abstract: Two sampling strategies are used for solving an errors-in-variables problem where the system as well as the white measurement noises are of a continuous-time nature. The sampling strategies are integrated sampling, and lowpass filtering followed by instantaneous sampling. Covariance relations are derived and systems of equations are formed for the data obtained from the two sampling strategies, and parameter estimators based on these relations and equations are proposed.

Keywords: Identification
Abstract: This paper deals with the problem of identifying linear errors-in-variables (EIV) models corrupted by white noise on the input and colored noise on the output. This allows to take into account the presence of both measurement errors and process disturbances. The proposed approach is based on a nonlinear system of equations whose unkowns are the system parameters and the input noise variance. The obtained set of equations allows mapping the EIV identification problem into a quadratic eigenvalue problem that, in turn, can be mapped into a linear generalized eigenvalue problem. The performance of the proposed approach is illustrated by means of Monte Carlo simulations and compared with those of existing techniques.

Keywords: Linear systems, Modeling
Abstract: The goal of this paper is to propose a method to compute a single transformation which aims to shift some initial Gramians of a set of state-space representations close to prefixed ones called target. The focus is done on sub-balancing (i.e. is target Gramians are the balanced ones).

Keywords: Linear systems, LMI's/BMI's/SOS's, Optimization
Abstract: This paper considers the synthesis of tracking control laws for discrete-time linear systems. A two-step approach that results in a sequence of quadratic contractive sets is proposed. Firstly, the quadratic sets are obtained by maximizing the size of the sets. Then, secondly, a conic partition is employed to construct continuous conewise linear control laws for each quadratic set while optimizing performance. Furthermore, a set-theoretic method is employed to extend the stabilizing law for the tracking control problem. As a result it yields scalable off-line computation of the control law. Moreover, this approach provides stability guarantees and constraints satisfaction. The effectiveness of the approach is demonstrated using an illustrative example.

Keywords: Linear systems, Constrained control, Uncertain systems
Abstract: In this article, the case is considered where a regulated output of an uncertain system with unknown disturbance input is constrained to lie between two specified time-varying bounds. It is supposed a controller has already been designed using any desired technique. This controller may not satisfy expected output constraints. The objective is to design saturations on the control signal so that the closed-loop is altered only when those specifications are not met. The theory is presented in the linear case with linear dependence on an unknown disturbance. Application to an uncertain launcher linear model with unknown wind disturbance is presented.

Keywords: Linear systems, Optimal control
Abstract: Problems of optimal pole placement for linear time invariant systems via state feedback have been studied for several decades. The minimum gain pole exact placement problem involves obtaining a feedback matrix that will assign a certain desired set of closed-loop poles, while also minimizing the gain (matrix norm) of the feedback matrix. Numerous methodologies have appeared in the literature to address the problem. In this paper, results from extensive experiments comparing the performance of a number of methods are reported. The robustness, runtime and accuracy of the pole placement achieved by each method are also compared. The results show some notable differences between the methods surveyed.

Keywords: Linear systems, Optimal control
Abstract: In this paper we analyze the properties of the set of solutions of the generalized continuous algebraic Riccati equation from a geometric perspective. In particular, we study the relationship existing between the solutions of the generalized Riccati equation and the output-nulling subspaces of the underlying system. This analysis reveals the presence of a subspace that plays an important role in the solution of the related optimal control problem.

Keywords: Observers for nonlinear systems, Aerospace, Autonomous systems
Abstract: In this paper, a new pose estimation solution for perspective vision system is presented and applied to a civil aircraft landing phase. Vision sensor is used to overcome the need for external technologies and runway knowledge. Two nonlinear observers are proposed for on-line estimation of the deviations of the aircraft wrt the runway. These nonlinear observers are based, first, on a high-gain approach, and then on a high-order sliding-mode approach, allowing robustness and finite time convergence. The originality of the presented results consists in estimating deviations with redundant informations in order both to increase efficiency of the observer and guarantee observability. Simulation results obtained on a realistic landing scenario are presented.

Keywords: Observers for nonlinear systems, Aerospace
Abstract: This paper studies a rigid body attitude tracking control problem with attitude measurements only, when angular velocity measurements are not available. An angular velocity observer is constructed such that the estimated angular velocity is guaranteed to converge to the true angular velocity asymptotically from almost all initial estimates. As it is developed directly on the special orthogonal group, it completely avoids singularities, complexities, or discontinuities caused by minimal attitude representations or quaternions. Then, the presented observer is integrated with a proportional-derivative attitude tracking controller to show a separation type property, where exponential stability is guaranteed for the combined observer and attitude control system.

Keywords: Observers for nonlinear systems, Applications in neuroscience, LMI's/BMI's/SOS's
Abstract: We present a model-based approach to estimate the mean embrane potentials (and their time-derivatives) of populations of neurons within cortical columns. We consider a general class of neural mass models for which we design local state observers. The synthesis relies on linear parameter-varying systems techniques and the observer gains are obtained by solving linear matrix inequalities. Simulations results are presented to illustrate the efficiency of the approach.

Keywords: Observers for nonlinear systems, Sliding mode control, Robotics
Abstract: In this paper a new method of designing a sliding mode observer for SCARA robot is proposed. This method is based on the construction of the singularity subspaces. The algorithm for estimating position and speed of the considered robotic manipulator is presented. Simulation results confirm the validity of our approach.

Keywords: Observers for nonlinear systems, Stochastic filtering, Robotics
Abstract: We consider the application of the Kalman Filter to systems with a certain type of measurement model. In addition to the state at the current time step, these measurements also depend on the state from one time step earlier. Although such measurement models are not encountered very often, they do appear in some practical control applications in robotic vision. In this paper, we derive a generalized Extended Kalman Filter from a modified form of the basic Bayes filter. The dependence of the measurement model on the previous state is made explicit through the law of total probability, which we include as an additional step in the standard prediction-correction cycle of the Bayes filter. We find that this dependence results in an increase in the measurement noise covariance. We present the mathematical formulation along with the proof of the filter equations. In the end, we demonstrate the filter through a simulation of Monocular SLAM with wide-baseline stereo measurements.

Keywords: Control over networks
Abstract: In this paper, a networked control system (NCS) over communication links with several communication effects such as packet losses, delays and sampling jitter is considered. Based on an appropriate modeling of the NCS and its links, a quadratic cost function is computed for evaluating and predicting the NCS performance for an arbitrary controller at given communication conditions. To validate the analysis, an NCS is implemented in which the motion of two mobile robots is controlled. The obtained empirical results validate the proposed NCS modeling and cost-based performance analysis.

Keywords: Control over communication, Optimization, Stochastic control
Abstract: The main contribution of this paper is the characterization of the limiting average performance of a system involving two agents who coordinate their actions which belong to a textit{continuous} set. One agent has complete and noncausal knowledge of the sequence of i.i.d. realizations of a random state X_0, which is called the system state and affects the common team payoff. For the other agent, two scenarios in terms of observation assumptions are considered: in the first scenario, the other agent has a strictly causal knowledge of the system state while in the second scenario it has no direct knowledge of the state at all. However, in both scenarios, the less informed agent can always observe a noisy and strictly causal version of the actions taken by the (most) informed agent. There exists no dedicated communication channel between the two agents, and thus, the informed agent can only communicate via its actions which in turn affect the common payoff, hence the term implicit communication. Thus, there is a tradeoff to be found for the informed agent between communicating information about the incoming realizations of the system state and maximizing the payoff at the current stage. We use this general framework and apply it to a specific cost function, namely the Witsenhausen cost function. Although the problem tackled differs from the famous Witsenhausen's counterexample, the authors believe interesting new connections which help to understand the corresponding open problem might be established over time. A numerical analysis is conducted to assess the Witsenhausen's cost for two sub-optimal classes of strategies.

Keywords: Control over networks, Fault detection and identification, Sensor and mesh networks
Abstract: A Multi-hop Control Network (MCN) consists of a LTI system where the communication between sensors, actuators and computational units is supported by a (wireless) multi-hop communication network and data flow is performed using scheduling, routing and network coding of sensing and actuation data. In this paper we extend our previous results in [D'Innocenzo et Al., CDC2013] on the Fault Detection and Isolation (FDI) problem over a MCN where the plant is a MIMO system and the communication nodes are subject to permanent failures and malicious intrusions. In particular, we provide necessary and sufficient conditions such that the FDI problem can be solved with much milder assumptions on the failure signal.

Keywords: Control over communication, Switched systems, Linear parameter-varying systems
Abstract: This paper addresses control and scheduling codesign problem for multiple control loops sharing a common communication and computation medium, known also as networked embedded control system (NECS) in literature. Besides the limited communication medium, physical limitations on control inputs and/or states are considered as well. To avoid unnecessary use of the limited communication medium, each control loop is equipped with a model-based event generator requesting a network access only if a necessity exists and thus minimizing the traffic load on the network. The NECS with uncertain but interval-bounded time-varying communication and computation delays is modeled as a discrete-time switched polytopic system. Based on the resulting model and the proposed model-based event generators, the codesign problem is then introduced and formulated as an LMI optimization problem which can be solved completely offline. The effectiveness of the codesign strategy is evaluated for networked control of an active suspension system. Noteworthy, the proposed strategy is generally applicable to discrete-time switched polytopic systems.

Keywords: Control over networks, Fault accommodation, Observers for linear systems
Abstract: This paper presents a secure and robust state estimation scheme for continuous-time linear dynamical systems. The method is secure in that it correctly estimates the states under sensor attacks by exploiting sensing redundancy, and it is robust in that it guarantees a bounded estimation error despite measurement noises and process disturbances. In this method, an individual Luenberger observer (of possibly smaller size) is designed from each sensor. Then, the state estimates from each of the observers are combined through a scheme motivated by error correction techniques, which results in estimation resiliency against sensor attacks under a mild condition on the system observability. Moreover, in the state estimates combining stage, our method reduces the search space of a minimization problem to a finite set, which substantially reduces the required computational effort.

Keywords: Discrete event systems, Fault diagnosis, Hybrid systems
Abstract: This paper deals with the analysis of critical observability for networks of Finite State Machines (FSMs). Critical observability is a property of FSMs that corresponds to the possibility of detecting whether the current state of an FSM is, or is not, in a set of critical states modeling unsafe operations. The study of this property is relevant in safety-critical applications, as for example Air Traffic Management (ATM) systems where the timely recovery of human operators errors and technical devices disruption is of primary importance in ensuring safety of the ATM procedures. In general, for checking this property, a critical observer is designed which detects on-line the occurrence of critical situations. When a large-scale network of FSMs is considered, the construction of such an observer is prohibitive because of the large computational effort needed. In this paper we present an approach based on bisimulation equivalence which reduces the original network to a smaller one while preserving the critical observability property. Further, we show that a critical observer designed for the reduced network can be utilized for the original network. The advantages of the proposed approach in terms of computational complexity are discussed in the paper.

Keywords: Mechatronics, Hybrid systems, Automotive
Abstract: In power-split transmission systems developed by Dana-Rexroth Transmission Systems, a crucial role is played by the adjustable mechanical couplings performed by clutches operated by means of suitable electrovalves. We propose here a reset control scheme with adaptive feedforward action to perfectly track a known smooth reference for the current flowing in the valves. Formal properties of the scheme are first established and then a simplified approximated version is proposed for a simple implementation on the real-time control system. We report on simulation and experimental results both illustrating illustrate desirable responses. Simulations also confirm the theoretical results about asymptotic convergence of the estimates to the values of the unknown valve parameters.

Keywords: Discrete event systems, Fault tolerant systems, Switched systems
Abstract: Reachability and state recovery for switched asynchronous sequential machines are investigated in this paper. The considered switched asynchronous machine is exposed to the adverse effect of adversarial inputs. The main objective is to design a state feedback controller so that the switched machine can recover the original state after going through unauthorized state transitions by faults. We propose a novel method of describing the reachability of the switched machine and the condition for the existence of a controller based on the proposed reachability matrices. An illustrative example is given to show the validity of the main results.

Keywords: Linear systems, Stability of hybrid systems, Switched systems
Abstract: We consider particular linear switched systems (LSS) with autonomous transitions, that we model by rectangular implicit representations. We design proper approximations for proportional and derivative descriptor variable feedback laws that were previously proposed for making unobservable (decoupling) the effect of the variable structure, and guarantee BIBO-stability for the switched controlled system. An example is used for illustration.

Keywords: Automata, Stability of hybrid systems, Switched systems
Abstract: We present a stability analysis framework for the general class of discrete-time linear switching systems for which the switching sequences belong to a regular language. They admit arbitrary switching systems as special cases.

Using recent results of X. Dai on the asymptotic growth rate of such systems, we introduce the concept of multinorm as an algebraic tool for stability analysis.

We conjugate this tool with two families of multiple quadratic Lyapunov functions, parameterized by an integer T >= 1, and obtain converse Lyapunov Theorems for each.

Lyapunov functions of the first family associate one quadratic form per state of the automaton defining the switching sequences. They are made to decrease after every T successive time steps. The second family is made of the textit{path-dependent} Lyapunov functions of Lee and Dullerud. They are parameterized by an amount of memory (T-1) >= 0.

Our converse Lyapunov theorems are finite. More precisely, we give sufficient conditions on the asymptotic growth rate of a stable system under which one can compute an integer parameter T >= 1 for which both types of Lyapunov functions exist.

As a corollary of our results, we formulate an arbitrary accurate approximation scheme for estimating the asymptotic growth rate of switching systems with constrained switching sequences.

Keywords: Concensus control and estimation, Distributed cooperative control over networks, Agents and autonomous systems
Abstract: We study the problem of optimal leader selection in consensus networks with noisy relative information. The objective is to identify the set of k leaders that minimizes the formation's deviation from the desired trajectory established by the leaders. An optimal leader set can be found by an exhaustive search over all possible leader sets; however, this approach is not scalable to large networks. In recent years, several works have proposed approximation algorithms to the k-leader selection problem, yet the question of whether there exists an efficient, non-combinatorial method to identify the optimal leader set remains open. This work takes a first step towards answering this question. We show that, in one-dimensional weighted graphs, namely path graphs and ring graphs, the k-leader selection problem can be solved in polynomial time (in both k and the network size n). We give an O(n³) solution for optimal k-leader selection in path graphs and an O(kn³) solution for optimal k-leader selection in ring graphs.

Keywords: Concensus control and estimation, Distributed estimation over sensor nets, Agents networks
Abstract: In this work we address the problem of optimal estimating the position of each agent in a network from relative noisy vectorial distances with its neighbors. Although the problem can be cast as a standard least-squares problem, the main challenge is to devise scalable algorithms that allow each agent to estimate its own position by means of only local communication and bounded complexity, independently of the network size and topology. We propose a gradient based algorithm that is guaranteed to have exponentially convergence rate to the optimal centralized least-square solution. Moreover we show the convergence also in presence of bounded delays and packet losses. We finally provide numerical results to support our work.

Keywords: Concensus control and estimation, Distributed estimation over sensor nets, Communication networks
Abstract: We consider the problem of solving a Laplacian system of equations Lx = b in a distributed fashion, where L is the Laplacian of the communication graph. Solving Laplacian systems arises in a number of applications including consensus, distributed control, clock synchronization, localization and calculating effective resistances, to name a few. We leverage our analysis on a randomized variant of Kaczmarz’s algorithm to propose a distributed asynchronous gossip algorithm with expected exponential convergence. We quantify the convergence rate depending solely on properties of the network topology, and further propose an accelerated version that scales favorably for larger networks. Our approach naturally extends to least-squares estimation of general linear systems where each row/column is assigned to nodes of a given network. Last but not least, we show that average consensus is a special case in our framework.

Keywords: Concensus control and estimation, Quantized systems, Switched systems
Abstract: This paper deals with a quantized version of a consensus dynamics in continuous-time, which is motivated by opinion dynamics applications. Under the assumption of all-to-all communication, we show existence and completeness of solutions, we characterize the equilibria, and we prove asymptotical convergence to a state of quantized consensus. For almost all initial conditions, the consensus value differs from the initial average by at most the quantizer precision. Furthermore, we discuss the implications of more general assumptions on the communication graph.

Keywords: Distributed control, Agents networks, Distributed cooperative control over networks
Abstract: One general principle of the theory of symmetric linear system is that a control problem can be solved by means of a generic controller if and only if it can be solved with a controller that preserves the symmetry properties of the system. In this paper, we specialize this general principle to the case of the distributed control of interconnected systems. Namely, we show that this class of systems admits a decentralized output feedback stabilizer if and only if it admits a controller of the same type that respects the symmetry of the graph automorphism group. We also show that a similar result holds for the consensus problem.

Keywords: Optimal control, Aerospace, Constrained control
Abstract: Time-optimal manoeuvres are the constrained time optimal slews which enable a spacecraft to attain a desired attitude in minimum time. The distinguishing feature of our work is that we consider arbitrary state transfer on the state space, not necessarily rest to rest manoeuvres. Further we work in a geometric framework, wherein the trajectories are analyzed on a cylinder. This report considers a special case in which the spacecraft is manoeuvering about a single axis, the initial and final orientation as well as momentum are specified. Pontryagin's maximum principle is applied to characterize the time optimal control map under control constraints. Initially, we consider rest to rest manoeuvres and later this is generalised to any given initial and final values of the momentum.

Keywords: Optimal control, Aerospace, Optimization algorithms
Abstract: We consider a family of the problems on maximization of the height of the vertical flight of a material point in the presence of a nonlinear friction and a constant flat gravity field under a bounded thrust and fuel expenditure. Using the maximum principle we obtain classification of trajectories (w.r.t. problem parameters) which are suspected to be optimal and check that for some classical rocket systems optimal control in our model is the classical bang-bang or bang-singular-bang one. We obtain some new types of ``potentially-optimal'' trajectories which should be investigated.

Keywords: Optimal control, Flexible structures, Optimization
Abstract: We consider the generation of time-optimal trajectories for an underactuated mechanical system including rigid body and vibrational modes. The considered model problem consists of two masses moving in a plane and linked together by springs. One of the masses is actuated in both directions, whereas the other one represents the tool center point (TCP) which should move on a given trajectory in the plane.

We formulate the trajectory planning as an optimal control problem (OCP) which is discretized into a large scale nonlinear program (NLP) solved by direct multiple-shooting using the CasADi toolkit and the NLP solver IPOPT. The optimal solution is compared to a classical jerk limited bang-bang solution based on the rigid body model but not taking into account the vibrational modes.

The comparison of both approaches leads to a paradigm shift: instead of focusing on the classical trade-off between performance (cycle time) and precision (residual vibrations), it is more appropriate to deal with trade-offs between modeling uncertainties and residual vibrations. The residual vibrations can be completely eliminated if its model is perfectly known.

Keywords: Optimal control, Linear parameter-varying systems, Mechatronics
Abstract: Harmonic oscillators are widespread in industrial applications. One of their key properties is the large amplification of excitation signals at their eigenfrequency - broadly known as resonance. This paper investigates how to excite harmonic oscillators such that resonance is reached in minimum time. We use Pontryagin's Minimum Principle to derive time-optimal control laws to reach resonance for both the undamped and damped harmonic oscillator. For the former our derivations even lead to a time-optimal feedback law given through a switching curve. For application purposes we compare this feedback law with a much simpler sliding-mode based control law for controlling a simplified model of an industrial vibratory device. In order to smoothly apply both control laws, additional state and parameter estimators are presented to address the time-varying nature of the application.

Keywords: Optimal control of communication networks, Control over networks, Fault tolerant systems
Abstract: A Multi-hop Control Network consists of a plant where the communication between sensors, actuators and computational units is supported by a (wireless) multi-hop communication network, and data flow is performed using scheduling and routing of sensing and actuation data. With the aim of rendering the system robust with respect to packet losses, we exploit network coding and redundancy in data communication (i.e. sending multiple copies of sensing and actuation data via multiple routing paths {associated to possibly different delays}) and assume that such data is re-combined as a weighed linear combination. The main contribution of this paper is to provide efficient computational methods to find the optimal choice of such weights {either to maximize or to constrain} a robustness metric based on the notion of Asymptotic Mean Square Stability. Since we observe that such metric induces an objective or constraint function that is highly non-linear, we propose an approximations that takes into account both the network parameters and the system dynamics and is based on the assumption that packet loss probabilities are much smaller than correct transmission probabilities and leverages the well known Gerschgorin disks. We than make a comparison with the optimal choice from the communication designer point of view, which neglects the system dynamics and is based on the idea of minimizing the quadratic error induced by the network on the actuation signal. The comparison of the above approximations quantitatively shows that the optimal choices from the points of view of control and communication theory are often at odds.

Keywords: Robust control, Robotics
Abstract: Absolute stability of bilateral teleoperation has been investigated under the assumption that both human and environment behave as passive systems. This robust design is required since human and environment must be considered as unknown systems at the design stage. If this approach is widely used in the literature, it leads to conservative conditions. Recently, teleoperation has been described in the integral quadratic constraint (IQC) framework, which provides powerful tools to develop less conservative description of both, human and environment. In this initial work, we consider that the environment can be described by a memoryless, monotone, and bounded nonlinearity. Then, Zames-Falb multipliers can be introduced to relax the conservatism of current state-of-the-art stability conditions. The usefulness of our result is shown in a 2-channel position-force teleoperation; where the well known lack of passivity of a PD-F controller is corrected by the use of Zames-Falb multipliers.

Keywords: Robotics, Autonomous robots
Abstract: This work solves the obstacle avoidance problem extending the Potential Field (PF) method for a mobile robot. The usual definition of the PF has been modified to have a field which is continuous everywhere. It is shown that the system has an attracting equilibrium at the target point, repelling equilibriums in the centers of the obstacles and saddle points on the borders. Those unstable equilibriums are avoided capitalizing on the established Input-to-State Stability (ISS) property of this multi-stable system. To escape a local minima this work makes the most of ISS property that is not lost for perturbations. And for small properly designed disturbances the global attractivity of the target point is proved. The proposed modification of the PF method is shown to be effective by simulation and then applied for unicycle-like mobile robots with additive input disturbances.

Keywords: Robotics
Abstract: Time delay in bilateral teleoperation is a challenging problem from the point of view of both system stability and transparency. Traditional wave variable based teleoperation method can theoretically guarantee system passivity with arbitrary large time delay but cannot provide good position and force tracking performance. In this paper, a novel wave variable teleoperation structure is first proposed which can achieve position and force tracking with only one time delay, then a predictor is utilized to further reduce the force tracking difference between master and slave side. In order to retain passivity of the whole teleoperation system, energy reservoir technique is used to constraint injected energy. Simulation studies show that the proposed structure provides stable and enhanced position and force tracking performance compared to traditional wave variable method with position tracking difference of only one time delay and even smaller force tracking delay thanks to the predictor used.

Keywords: Robotics, Autonomous robots, Sensor and signal fusion
Abstract: This work describes a novel solution to the mapping problem for a mobile robot moving in an unknown indoor environment. The proposed mapping technique provides a cells-based covering of the environment boundaries. No assumptions are made on the cells placement, therefore the covering is not forced to stay in a regular scheme, like in the grid-based approach. The main idea is to estimate the probability mass function of finding an environment boundary point in a given cell, and to use the obtained approximation of these points to build an environment map. The resulting mapping algorithm can be either used alone to solve the Simultaneous Localization and Mapping (SLAM) problem, or it can be used in series to an existing given SLAM algorithm to improve its performance. Numerical simulations show the effectiveness of the proposed mapping and SLAM strategies by comparing them with other SLAM techniques from the literature.

Keywords: Uncertain systems, Robust control, LMI's/BMI's/SOS's
Abstract: This paper provides synthesis conditions of robust state feedback control law for continuous-time linear time-invariant systems with polytopic uncertainty. By conveniently expressing the Lyapunov stability condition of the closed-loop system and using the elimination lemma, a new LMI synthesis condition with scalar searches is proposed. As main novelty, the scalar search is constrained to the interval (-1,1). Additionally, it is shown that the proposed relaxation contains the less conservative condition available in the literature as a particular case. Numerical examples illustrate the proposed approach.

Keywords: Robust control, Linear systems, Uncertain systems
Abstract: Sufficient conditions for stability of feedback interconnections of negative imaginary systems are derived via an integral quadratic constraint (IQC) approach. These extend existing results in the literature by exploiting the flexibility present at the static and infinite frequencies to reduce conservatism. Negative imaginary transfer functions with poles on the imaginary axis are accommodated using a recently generalised IQC-based robustness result. In particular, the negative imaginary property of systems is shown to give rise to IQCs on positive frequencies that are bounded away from zero and infinity. Additional quadratic conditions are introduced to take care of the IQCs near the DC and instantaneous gains of the systems.

Keywords: Robust control, LMI's/BMI's/SOS's, Stability of linear systems
Abstract: In this work, a procedure is presented for performance analysis intended to evaluate the resilience and H2 norm bound of discrete-time systems controlled by full-order dynamic feedback compensators. Acceptable performance is specified by disks in the complex plane within which the eigenvalues of the controller and the observer remain in the presence of perturbations in the controller and observer gains. Maximum gain perturbation bounds can be obtained based on the designer’s choices of controller and observer eigenvalue regions and the resulting H2-norm bound is calculated. The linear matrix inequality technique is used throughout the analysis process. Illustrative examples are included to demonstrate the effectiveness of the proposed methodology.

Keywords: Robust control, Reduced order modeling, Optimal control
Abstract: In this paper, a fast tube-based algorithm is proposed for robust model predictive control (RMPC) using an uncertain finite step response (FSR) input-output model with time-invariant bounded uncertainty. The use of an FSR model, in place of the high-dimensional state-space model, allows the assurance of good performance and constraint satisfaction while dramatically reducing the online cost of the controller. Bounds on the uncertain FSR coefficients are computed offline from the high-dimensional model or by performing step tests in the plant. Using these bounds, a tube-based RMPC algorithm is employed to guarantee the practical stability of the closed-loop system. The practicality of the algorithm is demonstrated by application to an infinite-dimensional example inspired by the field of tissue engineering.

Keywords: Identification, Nonlinear system identification
Abstract: This paper deals with a new practical approach to continuous-time parametric identification for dynamic models in Hammerstein form and its application in the context of estimating the dynamics of joint stiffness. The proposed methodology deals the static non-linearities as a linear combination of nonlinear basis functions, while the linear dynamic parts of the system are modeled by a parametric rational transfer function. The proposed identification method is enabled by the algebra of Volterra linear integral operators by a suitable design of kernels admitting finite-dimensional and internally stable state-space realizations. Simulation results show the effectiveness of the proposed continuous-time identification technique.

Keywords: Identification, Nonlinear system identification, Uncertain systems
Abstract: In dynamical systems context, a predictor is a method that provides an estimation of the future system output using past information of the system. An interval predictor provides an outer estimation of the future output. The center of this interval can be used as central or nominal prediction. A method to formulate interval predictors is to assume an unknown but bounded error in the system measurements. The aim of this work is to study the benefits of using a Reversed Huber's function as error function in this kind of predictors. A Reversed Huber's function is a convex function, piecewise linear near zero but quadratic for large values. The paper provides a nonparametric formulation of the interval predictor and shows by a real world example that the proposed predictor can improve the performance of the central prediction.

Keywords: Identification, Optimization
Abstract: This paper presents a paradigm for designing inputs to identify linear continuous-time SISO systems. The goal here is to design a band-limited spectrum that is D-optimal and satisfies certain input/output power constraints. The D-optimal criterion with input/output constraints lead to a set of optimal moments. In order to determine the spectrum from these moments, it is first approximated by a finite order sum of squares (s.o.s.) polynomial. It is shown that the coefficients of the s.o.s. polynomial and its moments are linearly related. These coefficients can be determined by minimizing the distance between the optimal moments and the moments of the finite order approximation under the constraint that the spectrum is non-negative. Alternatively, the linear relationship between the moments and coefficients of the spectrum can also be enforced as additional constraints along with the power constraints and non-negativity constraints in the D-optimal framework. A simulation example, with a 2 nd order system, is presented to illustrate the proposed method of determining the power spectrum and the input.

Keywords: Identification, Optimization algorithms, Optimization
Abstract: The nuclear norm is an effective proxy for matrix rank in a range of minimization problems, including subspace identification. Nuclear norm-based methods are implemented via iterative optimization methods and in problems with very noisy data the quality of the nuclear norm-based estimate may warrant the additional computation cost. We present two methods (based on the dual accelerated gradient projection and the alternating direction method of multipliers) for nuclear norm based subspace identification in the case where the data is given as irregularly spaced frequency samples.

Keywords: Identification, Signal processing
Abstract: The paper proposes a new frequency domain method for identifying linear dynamic errors-in-variables (EIV) models. The noise-free input is an arbitrary signal, not necessarily periodic and the input and output noises are additive and uncorrelated white processes. The method combines, in a frequency domain context, the characteristcs of the Frisch scheme and the properties of the Yule-Walker equations. The features of the method are illustrated by means of numerical examples.

Keywords: Identification, Statistical learning, Modeling
Abstract: A new Bayesian approach to linear system identification has been proposed in a series of recent papers. The main idea is to frame linear system identification as predictor estimation in an infinite dimensional space, with the aid of regularization/Bayesian techniques. This approach guarantees the identification of stable predictors based on the prediction error minimization. Unluckily, the stability of the predictors does not guarantee the stability of the impulse response of the system. In this paper we propose and compare various techniques to address this issue. Simulations results comparing these techniques will be provided.

Keywords: Delay systems, Observers for nonlinear systems
Abstract: In this work, it is shown that the results introduced in recent article that holds for full state measurement, can be extended to partial state measurement. In particular, it is proven that the combination of an observer with the new predictive scheme leads to a better disturbance attenuation than using the same observer but with the standard predictive scheme. Finally, some simulations illustrate the results for constant and time-varying disturbances.

Keywords: Delay systems, Nonlinear system theory
Abstract: We present a new backstepping result for control affine time-varying systems with input delays. The novelty of our work is in the bounds on the controls, and the facts that (i) one does not need to compute any Lie derivatives to apply our controls and (ii) the controls have no distributed terms.

Keywords: Delay systems, H2/H-infinity methods, Control over networks
Abstract: Sufficient conditions are presented for a system with input delays having finite L2-gain. The bounded real lemma conditions are infeasible when the actual and the delayed values of the input act on the system simultaneously. Considering the actual and delayed inputs as two independent inputs is shown to lead to very high upper-bound of the true L2-gain. Several approaches are presented involving a scaling method which is based on upper-bounds of the delayed signal norms; a method which transforms the input delays to state delays with the help of introducing additional dynamics; and a method based on integral quadratic constraints. Both time-invariant and time-varying delays are considered. The methods are evaluated on an example of interconnected vehicles.

Keywords: Delay systems, Robust control, LMI's/BMI's/SOS's
Abstract: This paper is dedicated to the stability analysis of a class of distributed delay systems with a non constant kernel. By the use of appropriate orthogonal polynomials, this kernel is expressed as the sum of a polynomial and an additive bounded function. The resulting system is then modelled as an interconnected system between a nominal finite dimensional linear system and a infinite dimensional system. This last system is considered as a structured uncertainty and embedded into some well defined structured uncertainties. Appropriate robust control tools, i.e. quadratic separation, are then used to address the stability issue. Finally, numerical examples show the effectiveness of the proposed method.

Keywords: Delay systems, Observers for linear systems
Abstract: New delay-dependent conditions of positivity for linear systems with time-varying delays are introduced. These conditions are applied to interval observer design for systems with time-varying delays in the state equations and in the measurements.

Keywords: Aerospace, Predictive control for nonlinear systems, Hybrid systems
Abstract: This paper studies an important aspect of attitude control for a launcher's upper stage: the minimum impulse bit (MIB), that is, the minimum torque that can be exerted by the thrusters. We model this effect using principles of hybrid systems theory and we design a hybrid model predictive control scheme for the attitude control of a launcher during its long coasting period, aiming at minimizing the number of thrusters' actuations. We apply the proposed methodology to a nonlinear model of a typical upper stage with multi-payload capability.

Keywords: Aerospace, Predictive control for linear systems, Linear time-varying systems
Abstract: The paper proposes a predictive control law for orbital rendezvous hovering phases. The proposed algorithm provides an impulsive control that steer the vehicle to a set of the periodic relative orbits enclosed in a hovering zone. The control law is computed by determining a point lying in the intersection between a semi-algebraic set and a hyperplane using an alternating projections algorithm. The present work proposes several improvement compare to previous published works based on a two-impulse strategy: the control consists of one impulse instead of two, the periodic and admissible orbit that the chaser is steered to is not defined a priori, and the budget and saturation conditions are taken into account. The efficiency of the proposed predictive control algorithm is bench tested on a nonlinear simulator.

Keywords: Aerospace, Lyapunov methods, UAV's
Abstract: This paper presents a track guidance algorithm verified with Lyapunov stability theory. In order to minimize the track error between a pre-assigned flight path and the position of an UAV, its heading or yaw rate is guided to keep a certain distance ahead on the flight path. The proposed guidance algorithm is a spatial version of the first order dynamics for a time-dependent system. An invariant tracking performance is guaranteed in a spatial domain so that a constant flight trajectory pattern is obtained regardless of a speed of the vehicle. Crucial design parameters are a spatial constant and a gain Kp. The spatial constant determines a shape of the convergence to an assigned flight path and the gain Kp affects convergence stability of an UAV trajectory. Hence, Lyapunov stability theory and LaSalle's invariance principle are applied to show that the guidance algorithm is asymptotically stable and to decide the available boundaries of the spatial constant and the gain Kp. Reference flight trajectories are designed based on a two-dimensional vehicle model, and the proposed algorithm is verified by numerical simulations using six degree of freedom rigid body UAV dynamics with MATLAB/Simulink Aerosim Blockset.

Keywords: Aerospace, Transportation systems, Autonomous systems
Abstract: This paper introduces the payload stabilization method for safe payload transportation. We assumed that the vehicle and the payload are connected by wires, which is called the slung-load system. For the slung-load system, the wire generates various constraint forces between the vehicle and the payload. Various types of slung-load systems can be defined by various numbers of vehicles and payloads, but in this research, we used the slung-load system that is composed of only one vehicle and one payload. To model the slung-load system, we applied the Udwadia-Kalaba equation (UKE) to account for the slung-load system with various constraint forces. To transport the payload safely to destination point from the starting point, we suggest the payload stabilization method that modifies the structure of position controller of a quad-rotor. Controller gain design processes are also performed. Lastly, using the derived system model based on the UKE, simulation and flight tests are performed to demonstrate the performance of the payload stabilization method we suggested. Additionally, a particular unmanned aerial vehicle of quad-rotor is chosen as the main platforms of the entire system due to its advantages such as simple control logic implementation and maintainability.

Keywords: Autonomous systems, Aerospace, Sensor and signal fusion
Abstract: A nonlinear attitude estimator is considered in this paper. The estimator shares several properties with estimators previously considered including ease of implementation and guaranteed asymptotic stability. The proposed algorithm is based on Poisson’s equation and propagates the attitude estimate using measurements taken by typical low-cost sensing systems. Rate gyro bias estimation is also considered. The estimator structure is derived using an alternate attitude error function, which results in desirable convergence properties. These properties are demonstrated in simulation where the proposed attitude estimator is shown to achieve a faster convergence rate than an attitude estimator considered in the literature.

Keywords: Feedback linearization, Aerospace, Robust control
Abstract: Control of nonlinear systems with uncertain information about the nonlinearity poses significant challenges, as any mismatch between the true nonlinearity and the one assumed for controller design can result in performance degradation or even instability. In this paper we merge optimal nonlinearity approximations with controller dissipativity properties to find emph{explicit} expressions for stabilizing output feedback controllers and estimates of the basin of attraction of the equilibrium in the origin. The control of an aeroelastic system with structural nonlinearity is used as a case study to discuss in detail the proposed approach and to demonstrate its effectiveness.

Keywords: Automotive, Algebraic/geometric methods, Nonlinear system theory
Abstract: A new model-free setting and the corresponding ``intelligent'' P and PD controllers are employed for the longitudinal and lateral motions of a vehicle. This new approach has been developed and used in order to ensure simultaneously a best profile tracking for the longitudinal and lateral behaviors. The longitudinal speed and the derivative of the lateral deviation, on one hand, the driving/braking torque and the steering angle, on the other hand, are respectively the output and the input variables. Let us emphasize that a ``good'' mathematical modeling, which is quite difficult, if not impossible to obtain, is not needed for such a design. An important part of this publication is focused on the presentation of simulation results with actual and virtual data. The actual data, used in Matlab as reference trajectories, have been obtained from a properly instrumented car (Peugeot 406). Other virtual sets of data have been generated through the interconnected platform SiVIC/RTMaps. It is a dedicated virtual simulation platform for prototyping and validation of advanced driving assistance systems.

Keywords: Automotive, Identification, Modeling
Abstract: In this study a method for effective parameter estimation of driver model is proposed. The driver dynamic model during steering task is derived. An optimal experiment is designed allowing to optimize the precision of the results. The proposed approach is confirmed on experiment data.

Keywords: Automotive, Mechatronics, Identification
Abstract: Dry clutches are widely used in automated manual transmissions. In order to obtain “good” clutch engagements a precise torque transmissibility estimation is crucial. In this paper, a temperature-dependent model of the torque transmissibility characteristic for dry dual clutches is proposed. Dynamic models of the clutches thermal evolutions are determined and linked to the characteristics of the mechanical components influencing the torque transmissibility. Numerical simulations obtained by considering a realistic powertrain model with a dry dual clutch transmission and a corresponding multivariable controller, confirm the effectiveness of the proposed model.

Keywords: Sliding mode control, Autonomous systems, Autonomous robots
Abstract: Autonomous vehicle has been one of the highly investigated topics in both academia and industry. A car that can drive without the driver’s intervention changes not only the dynamics of the automobile industry but also everyday life of individuals. This paper demonstrates the road tracking capability of a steering controller that is designed based on the human driver model and a controller using a sliding mode control scheme. In addition, a novel controller that incorporates the preview control into the sliding mode controller is introduced. Using a bicycle model and a linear lateral tire force model, performances of these controllers for a double lane change maneuver are evaluated through simulations. The results illustrate the characteristics and limitations of the controllers and provide a strong ground for designing a successful road tracking controller for autonomous vehicle.

Keywords: Automotive, Modeling, Observers for nonlinear systems
Abstract: Achieving steering feel that is independent from the inherent mechanical friction is a requirement that prevails in modern electric power steering (EPS) development. Friction affects both the vehicle performance (objective) and the steering feel (subjective). The motivations in reducing and ultimately canceling friction are a simpler development and tuning of the EPS control. This paper proposes a control strategy for the compensation of the friction in a column-assist EPS (C-EPS). It is based on a model of the column, which includes the slip and load dependent friction of the reduction gear. Experimental results on a mass-produced C-EPS validate that the closed-loop performance is similar to that of a linear, frictionless system.

Keywords: Automotive, Uncertain systems, Sliding mode control
Abstract: In this paper, an electronic throttle valve system is considered. The mathematical model describing the motion of the valve plate is subject to uncertainties and external disturbances. The throttle plate angle is intended to track given reference profiles. A control scheme consisting of an observer and a state controller, which both are based upon the super-twisting technique, is proposed. Effectiveness and superiority of the approach are illustrated in numerical simulation using a comparison with a classical control approach. The presented feedback loop is implemented in an engine control unit. Results obtained through comprehensive tests on an industrial combustion engine test bench are given.

Keywords: Hybrid systems, Stability of nonlinear systems, Robust control
Abstract: We consider the design of self-triggered controllers for Lipschitz nonlinear systems subject to additive disturbances. The goal of the controller design is to avoid measurements and input changes as long as possible, while guaranteeing good performance subject to the worst case disturbances. Furthermore, the controller should be practically implementable. The controller is based on the emulation of a continuous-time feedback and a triggering mechanism employin simple predictions of the uncertain state evolution. An example illustrates the approach.

Keywords: Cooperative autonomous systems, Hybrid systems, Agents networks
Abstract: This paper focuses on consensus in networks partitioned in several clusters. It uses the multi-agent framework in which the network is seen as a sum of interconnected subsystems called agents. We assume that each agent updates its state continuously by taking into account the states of some other agents belonging to the same cluster. This protocol allows reaching only local agreements in the network. In order to get consensus we endow an agent per cluster with the capacity to discretely interact outside its own cluster. The discrete interaction of one agent with agents from other clusters is modeled as a state jump or reset. The inter-clusters interactions are activated by using event dependent rules. The goal of the paper is to design event triggering reset strategies that guarantee the consensus is achieved. No global dwell time separating the reset instants is imposed but we show that a dwell time per cluster is ensured by the proposed reset strategies.

Keywords: Hybrid systems, Switched systems, Robust control
Abstract: Continuous-time reset control is a hybrid feedback control of a continuous-time controller and a state dependent reset rule. In this paper, a synthesis of the purely output-feedback linear reset control is considered via LMI approach. The reset control consists of the continuous-time controller and the reset law determined by jump and reset matrices. Different from the state-feedback case in the author's previous result, not only the controller but the reset law should depend on the measured output and the controller state only. This can be done by introducing the characteristic structure into the jump and reset matrices. The sufficient condition for the existence of L2 stabilizing output-feedback reset control is also discussed.

Keywords: Control over networks, Optimal control, Linear systems
Abstract: Recent research advocates that replacing the periodic communication paradigm by an event-triggered paradigm can have significant benefits for control systems. While in many event-triggered control solutions transmission decisions are based on full state information, in most applications only partial state information is available for feedback (output measurements). Here we propose an optimization-based output-feedback event-triggered solution for linear discrete-time systems which guarantees a performance bound with respect to periodic control, while reducing the communication load. Performance is measured by a quadratic cost. The usefulness of the results is illustrated through a numerical example.

Keywords: Control over communication, Output feedback, Sampled data control
Abstract: Although setpoint tracking problems are common in practice, they are still widely unexplored in the eventtriggered control framework. Therefore, an event-triggered PI control design strategy with guaranteed performance for networked control systems is proposed in this paper. The integral state of the proposed PI controller is updated at every sampling instant. The update is done by the proposed event generator and sent with new measurements to the PI controller when necessary. Based on a discretized system model and a quadratic cost function, the event-triggered PI control design problem is introduced and formulated as an LMI optimization problem. The effectiveness of the proposed strategy is then experimentally evaluated and compared with other existing strategies in the literature for a case study.

Keywords: Control over networks, Stability of hybrid systems, Nonlinear system theory
Abstract: We synthesize robust output-based event-triggered controllers for a class of nonlinear systems subject to external disturbances and noises in both the plant measurement and the control input. We follow an emulation approach to this purpose: we first assume that we know a robust feedback law in continuous time, we then take into account the sampling and we explain how to construct the triggering condition to preserve stability. The triggering strategy enforces a minimum time between two consecutive transmissions by combining ideas from event-triggered control and periodic sampled-data control. The closed-loop system is shown to satisfy an input-to-state stability (ISS) property with respect to the external disturbances, the noises as well as their time-derivatives. The analysis reveals a tradeoff between the enforced minimum inter-transmission time and the magnitude of the ISS gains. The results are also new in the particular case where the triggering condition is only time-dependent as in traditional sampled-data control.

Keywords: Distributed cooperative control over networks, Large-scale systems, Hybrid systems
Abstract: This paper presents a self-organizing networked controller for the disturbance attenuation in physically interconnected systems. To avoid large control errors, the local controllers send information to the other local controllers, if the coupling input to their corresponding subsystem exceeds a given bound. As a consequence, the communication topology is adjusted to the current control task and presents an image of the acting disturbances at all times. It is proven that the self-organizing controller leads to a practically stable overall system and that the effect of the disturbance can be bounded by an appropriate choice of the information activation condition. The approach is demonstrated by its application to a thermofluid process.

Keywords: Distributed cooperative control over networks, Agents and autonomous systems, Switched systems
Abstract: This paper concerns the design of the communication structure of a self-organizing networked controller of multi-agent systems. The agents have individual dynamics, are unidirectionally coupled within a basic communication structure and have to follow a command signal. To avoid large control errors and to simultaneously reduce the communication effort, the agents request additional information from other agents, only if their local control error exceeds a given bound. As a consequence, the communication topology of the overall system is adjusted to the current control task and, as a result of the self-organization, presents an image of the currently acting command signal. A quantitative bound of the control error is used to develop an algorithm for designing the communication structure and the activation condition for the information request. The approach is demonstrated by its application to a robot formation problem.

Keywords: Distributed cooperative control over networks, Distributed estimation over sensor nets
Abstract: This paper studies the distributed control and estimation of multi-agent systems based on bearing information. In particular, we consider two problems: (i) the distributed control of bearing-constrained formations using relative position measurements and (ii) the distributed localization of sensor networks using bearing measurements. Both of the two problems are considered in arbitrary dimensional spaces. The analyses of the two problems rely on the recently developed bearing rigidity theory. We show that the two problems have the same mathematical formulation and can be solved by identical protocols. The proposed controller and estimator can globally solve the two problems without ambiguity. The results are supported with illustrative simulations.

Keywords: Distributed cooperative control over networks, Traffic control, Distributed control
Abstract: This paper considers control of networks modeled by a directional graph. We propose a leader-follower paradigm for coordinating local feedback controllers in a large network of interacting components, where most components - the followers - can handle local load without problems, but where a few heavily loaded components - the leaders - are close to saturation. The leader-follower paradigm partitions the network in subsystems, with one leader in each subsystem. Global performance can be improved if followers control their output variables so that the resulting input variables for their leader improve the performance at their leader. The leader-follower approach is explained using as a case study the coordination of the switching times of traffic lights in an urban traffic network. The local feedback controller at a leader uses local traffic data only, selfishly minimizing local queues by selecting the switching times of its traffic lights. Local controllers at follower intersections minimize a local delay, but also take into account specifications generated by their leader. Simulations show that this approach can improve system performance. The flexible feedback nature of this leader-follower control paradigm in one subsystem of the overall network makes it a good candidate as a building block for a scalable hierarchical controller for the complete network.

Keywords: Distributed cooperative control over networks, Network analysis and control
Abstract: We present a distributed resource allocation strategy to control an epidemic outbreak in a networked population based on a Distributed Alternating Direction Method of Multipliers (D-ADMM) algorithm. We consider a linearized Susceptible-Infected-Susceptible (SIS) epidemic spreading model and present a distributed solution in which agents in the network are able to locally compute their optimal allocation of vaccination resources (for prevention) and antidotes (for treatment) to control the contagion. We express our epidemic control condition as a spectral constraint involving the Perron-Frobenius eigenvalue, and formulate the resource allocation problem as a Geometric Program (GP). Next, we separate the network-wide optimization problem into subproblems optimally solved by each agent in a fully distributed way. We conclude the paper by illustrating performance of our solution framework with numerical simulations.

Keywords: Distributed cooperative control over networks, Optimization algorithms, Optimization
Abstract: We describe and present a general class of synchronous nonlinear increase-decrease algorithms which can be applied in any situation in which a limited resource has to be distributed amongst a group of users. No inter-agent communication is required and the algorithms are provably convergent. This work unifies and generalises a number of recent papers on this topic.

Keywords: Energy systems, Modeling, Power plants
Abstract: Heat pumps have recently received increasing interest due to green energy initiatives and increasing energy prices. In this paper, a nonlinear dynamic model of a single-effect LiBr-water absorption cycle heat pump is derived for simulation and control design purposes. The model is based on an actual heat pump located at a larger district heating plant. The model is implemented in Modelica and is based on energy and mass balances, together with thermodynamic property functions for LiBr and water and staggered grid representations for heat exchangers. Model parameters have been fitted to operational data and different scenarios are simulated to investigate the operational stability of the heat pump. Finally, this paper provides suggestions and examples of derivation of lower order linear models for control design.

Keywords: Energy systems, Process control, Decentralized control
Abstract: This paper investigates decentralized control structures for absorption cycle heat pumps and a dynamic nonlinear model of a single-effect LiBr-water absorption system is used as case study. The model has four controllable inputs, which can be used to stabilize the operation of the heat pump under different load conditions. Different feasible input-output pairings are analyzed by computation of relative gain array matrices and scaled condition numbers, which indicate the best pairing choice and the potential of each input-output set. Further, it is possible to minimize the effect of cross couplings and improve stability with the right pairing of input and output. Simulation of selected candidate input-output pairings demonstrate that decentralized control can provide stable operation of the heat pump.

Keywords: Energy systems, Output regulation, Modeling
Abstract: In heat networks, energy storage is a viable approach to balance demand and supply. In such a network, a heat carrier is used in the form of water, where heat is injected and extracted through heat exchangers. The network can transport and store heated water in stratification tanks to shift loads in time. A setup is considered, which includes a single producer with a storage tank and multiple consumers. For this topology, a model is derived consisting of differential equations that describe temperature and volume dynamics. We design a controller such that both the volume and the temperature converge to specified setpoints. The extracted heat that is demanded is unknown, while the flow rates passing through the heat exchanger of the consumers are set to a measurable constant. We regard both the flow rates and the heat injection of the producer as the control input. The controller uses techniques from output regulation, such that the prescribed setpoints are reached while satisfying the demand. We prove that under the proposed controller all solutions are bounded and convergence to the desired setpoints. Finally a case study is discussed and numerical simulations are provided.

Keywords: Energy systems, System reconfiguration, Predictive control for linear systems
Abstract: Inefficient design and operation of building heating systems can have a large impact on global energy consumption. Traditional heuristic approaches often supply excess heat and cannot adapt to faults and changes in the building and heating system. Model Predictive Control (MPC) based strategies can incorporate future building usage and weather conditions to achieve more efficient heating.

While MPC can produce an improved performance over standard strategies, many approaches taken in the literature are not easily scalable and do not allow for intuitive reconfiguration. Two possible MPC strategies for control of a building heating system are designed and compared here. In the first strategy, the thermal comfort of the occupants of the building is balanced with the energy use in a single objective function. In the second strategy, a lexicographic, multi-objective formulation is used to split the competing goals of energy reduction and thermal comfort. The strategies are assessed in a validated simulation platform in terms of energy efficiency, comfort performance, scalability and reconfigurability in times of system changes or faults.

Keywords: Predictive control for linear systems, Energy systems, Constrained control
Abstract: In this paper the performance of Model Predictive Control (MPC) and PID based strategies to optimally recover waste heat using Organic Rankine Cycle (ORC) technology is investigated. First the relationship between the evaporating temperature and the output power is experimentally evaluated, concluding that for some given heat source conditions there exists an optimal evaporating temperature which maximizes the energy production. Three different control strategies MPC and PID based are developed in order not only to maximize energy production but to ensure safety conditions in the machine. For the case of the MPC, the Extended Prediction Self-Adaptive Control (EPSAC) algorithm is considered in this study as it uses input/output models for prediction, avoiding the need of state estimators, making of it a suitable tool for industrial applications. The experimental results obtained on a 11kWe pilot plant show that the constrained EPSAC-MPC outperforms PID based strategies, as it allows to accurately regulate the evaporating temperature with a lower control effort while keeping the superheating in a safer operating range.

Keywords: Energy systems, Optimization algorithms, Decentralized control
Abstract: The primary goal of an energy management system (EMS) in power networks is to balance the supply and demand in a cost efficient manner given its operating horizon, and uncertainties in generation due to renewable generators and in demand. This goal is formulated as the economic dispatch problem. A centralized energy management system faces issues in scalability due to introduction of new generator or storage units and in robustness due to failures in some of the entities in the grid including the EMS itself. To alleviate these complexities a versatile decentralized energy management system (d-EMS) is developed. The d-EMS embeds a decentralized solution to the economic dispatch problem (EDP) based on the alternating direction method of multipliers (ADMM) inside a decentralized implementation of the receding horizon control. The ADMM based algorithm solves the EDP for the scheduling horizon. The receding horizon control allows the system to adapt to changes in the forecasts and network configuration. Decentralized protocols to handle changes to the communication network of devices is provided. These device failure and addition protocols entail network information updates only, thanks to the simple initialization of the ADMM algorithm.

Keywords: Genetic regulatory systems, Biological systems, Cellular dynamics
Abstract: Riboregulators represent an important class of genetic regulatory devices whose behaviour is programmable by specification of the underlying nucleic acid sequence. Both natural and synthetic riboregulators are often modelled as static devices that activate or repress potential RNA binding sites. Individual riboregulators are thereby functionally similar to linear gains found in electrical systems. While in silico design tools have been developed to maximize their in vivo performance, design of dynamic behaviour has yet to be considered. Herein a simple riboregulator design is proposed that appends programmable dynamic behavior to existing riboswitches. Specifically, the resulting device is shown to produce an all-or-nothing response. Simple design rules for this device are derived.

Keywords: Genetic regulatory systems, Stochastic systems, Computational methods
Abstract: We investigate the possibility of performing stochastic simulation of a synthetic gene circuit that includes a cell-to-cell communication system with an intracellular feedback control circuit. We propose an implementation of the CLE stochastic simulation method that makes possible to simulate gene synthetic circuits involving cell-to-cell communication. We find the minimum number of samples ensuring the statistic moments over the population do not degrade. This allows us to reduce the total simulation time and improve the efficiency of the method. Also we reveal that one realization of the population of interconnected cells, provided there is enough time to perform the time average, is useful to obtain representatives of the long-term moments of the population. Finally we show the potential of the improved approach by performing an analysis of the influence of the parameters in the LuxR promoter on the noise strength of the population.

Keywords: Biological systems, Biomolecular systems, Identification
Abstract: This paper proposes a set-based parameter identification method for biochemical systems. The developed method identifies not a single parameter but a set of parameters that all explain time-series experimental data, enabling the systematic characterization of the uncertainty of identified parameters. Our key idea is to use a state-space realization that has the same input-output behavior as experimental data instead of the experimental data itself for the identification. This allows us to relax the originally nonlinear identification problem to an LMI feasibility problem validating the norm bound of an error system. We show that regions of parameters can be efficiently classified into consistent and inconsistent parameter sets by combining the LMI feasibility problems and a generalized bisection algorithm.

Keywords: Biological systems, Modeling, Nonlinear system theory
Abstract: We developed a discrete time, structured, mathematical model describing the epidemiology of X-linked recessive diseases, a class of genetic disorders. The model accounts for both de novo mutations and distinct reproduction rates of procreating couples depending on their health conditions. We found the exact solution to the model when de novo mutations are not significant and negligible reproduction rates are assigned to affected males. Moreover, relying on Lyapunov's direct method, we proved asymptotic stability properties of model equilibrium points in case of significant de novo mutations and identical reproduction rates assigned to all procreating couples. We provide examples of model's application to relevant X-linked recessive diseases.

Keywords: Biomolecular systems, Nonlinear system theory
Abstract: Developing tools to understand biomolecular systems is important both for the analysis of naturally occurring systems as well as for the design of new ones. However, there is a need to develop new tools suited for analysis of such systems and to adapt existing tools used in other similar contexts. Here, we apply the method of sinusoidal-input describing function for the analysis of such systems using a combination of analytic and computational means. Using this technique, we approximate the input-output response of simple representative biomolecular signaling systems exhibiting different saturating as well as hysteretic input-output maps. We investigate the dependence of this approximation on system parameters. Finally, we estimate the error involved in these approximations finding that they are always better than standard linearization. These results provide system approximations that can help to compute system response to other periodic inputs as well as analysis and design of cell behaviour such as limit cycles and frequency entrainment.

Keywords: Applications in neuroscience, Emerging control theory
Abstract: This paper proposes a model and algorithm to compute the time encoded output of a linear filter directly from the time encoded input. The time encoding machine considered in this study is the integrate-and-fire neuron model. The proposed direct encoding approach is much faster than the indirect approach, involving simulating the filter output and subsequent encoding. A numerical simulation study is used to illustrate the approach.

Keywords: Optimal control, Energy systems, Hybrid systems
Abstract: Technological advances have made wireless sensor nodes cheap and reliable enough to be brought into various application domains. The limited energy capacity of sensor nodes is the key factor that restricts their lifespan. In this paper, a Predictive Control strategy for Dynamic Power Management of a set of wireless sensor nodes is proposed. The control formulation is based on Model Predictive Control with constraints and binary optimization variables, leading to a Mixed Integer Quadratic Programming problem. The proposed control algorithm guarantees services and performances levels with a minimum number of active nodes and/or a minimum load on such components. The strategy is evaluated on a real test-bench with wireless sensor nodes equipped with batteries and harvesting systems. Experimental results show the effectiveness of the proposed control method.

Keywords: Optimization, Energy systems, Process control
Abstract: This article presents an efficient implementation of a compressor load sharing optimization problem. In a typical gas pipeline application several compressor units are interconnected and operated together to meet station set point requirements. Load sharing optimization solves the problem of distributing the load among several compressors while at the same time satisfying operational constraints, user constraints and minimizing the consumption of the station. The structure of the problem is special and can therefore be exploited by formulating and solving the problem using the alternating direction method of multipliers (ADMM). The potential of this approach is assessed through simulations using field data from a large compression station with variable-speed gas turbine driven gas compressors. Despite being based on code generation, the ADMM implementation is about ten times faster than a state-of-the-art active set method while at the same time having a much lower memory footprint.

Keywords: Optimization, Energy systems, Uncertain systems
Abstract: Nowadays, elevators are equipped with storage devices to ensure autonomy in case of grid failure. This paper presents a method that takes advantage of these storage devices to optimize energy consumption and cost. The optimization is achieved by two controllers: a high-level one (using linear programming) and a low-level one (using simple rules). Preliminary results indicate that this method is 35% better than a naive rule-based approach in our context.

Keywords: Optimization, Energy systems, Supervisory control
Abstract: In this paper, we present a simple modelling approach for a thermal system, which consists of heating, ventilation, air conditioning system (HVAC) and a vapor compression cycle (VCC) system, with one loop heat recovery. In addition a simple model for water tank is presented, in which the reclaimed heat is stored and/or which can be used for heating purposes of the building. We present a dynamic optimization algorithm that according to the received price signal, occupancy information and ambient temperature minimizes the operation cost of the whole system and distributes set points to local controllers of supermarkets subsystems. We find that when reliable information about the high price period is available, it is profitable to use the refrigeration system to generate heat during the low price period, store it and use it to substitute the conventional heater during the high price period.

Keywords: Power plants, Energy systems, Predictive control for nonlinear systems
Abstract: Fast response of hydraulic turbines is a key condition for the integration of renewable sources of energy. In this paper, a new start-up strategy is proposed for hydraulic turbines prone to "S" instability. This method is based on a complete nonlinear model of the turbine together with the upstream water pipe. More precisely, the control strategy is based on a gain scheduling approach that is computed using finite horizon predictive control. This yields a state feedback control law that tracks a time-optimal trajectory that is computed based on the nonlinear model of the system. Simulations are shown to assess the efficiency of the proposed law and its robustness to model discrepancies.

Keywords: Optimization, Optimal control, Energy systems
Abstract: Demand side management in the future smart grid requires new players in the electricity markets. We assume a player, the so-called aggregator which aims to utilize the flexibility in large-scale consumers with thermal energy storage. An aggregator design is proposed to mange the power consumption of flexible demands during a certain period of time. The setup consists of an MPC-like controller to optimally operate the consumption units for upward and downward regulating power provision. To handle the uncertainties arise from model mismatch, a series of feedback loop are also considered along with the central optimization. We formulate the problem for two specific case studies which are supermarket refrigeration systems and chiller systems in conjunction with ice tanks. Simulation results are provided for these specific consumers.

Keywords: Autonomous systems, UAV's, Cooperative control
Abstract: This paper looks at the design of a distributed control law to solve the formation shape control problem using bearing-only constraints in two- dimensional space while inter-agent collision between the neighbor agents is avoided. We assume each agent can measure the relative position and is only given the desired bearing (and not the distance) to its neighbors in some local coordinate system attached to the agent. We use a relaxed control law that allows each agent to move on any direction on a half-plane to achieve the desired formation shape, and impose some conditions on the motion of the agents such that no collision occurs between the neighbor agents. Simulation results are given that show the performance of the algorithm.

Keywords: Autonomous robots, Fault tolerant systems
Abstract: Autonomous mobile machines use onboard sensors for navigation and obstacle avoidance. The accuracy of the sensor data in global frame is however dependent on the localization accuracy of the machine. Simultaneous localization and mapping algorithms (SLAM) are widely used with 3D laser scanners for mapping the world. They use scan matching algorithms to solve the accuracy problem by matching prior sensor data of the environment with the newly acquired data. However matching scans is not always possible. Insufficient amount of prior data or too few features in the scan can prevent the scan matching algorithm from finding a match. Thus it is important that also the mapping algorithm is tolerant to some degree of error in localization and calibration. We present a method for generating obstacle maps from smaller data segments at a time, thus making the mapping system more tolerant to navigation and calibration errors. The obstacle mapping method is tested with modified Avant multipurpose loader.

Keywords: Sensor and mesh networks, Agents and autonomous systems, Robotics
Abstract: This paper presents the design, analysis, and validation of a globally exponentially stable (GES) filter for tridimensional (3-D) range-only simultaneous localization and mapping in sensor networks with moving object tracking. For observability analysis purposes, two nonlinear sensor-based dynamic systems are formulated resorting only to exact linear and angular kinematics and a motion model for the target. A state augmentation is exploited that allows the proposed formulation to be considered as linear time-varying without linearizing the original nonlinear systems. Constructive observability results can then be established, leading naturally to the design of a Kalman Filter with GES error dynamics. These results also provide valuable insight on the motion planning of the vehicle. Simulation results demonstrate the good performance of the algorithm and help validate the theoretical results, as well as illustrate the necessity of a proper trajectory.

Keywords: Autonomous systems, Robotics
Abstract: This paper proposes a sampling based planning technique for planning maneuvering paths for semi-autonomous vehicles, where the autonomous driving system may be taking over the driver operation. We use Rapidly-exploring Random Tree Star (RRT*) and propose a two-stage sampling strategy and a particular cost function to adjust RRT* to semi-autonomous driving, where, besides the standard goals for autonomous driving such as collision avoidance and lane maintenance, the deviations from the estimated path planned by the driver are accounted for. We also propose an algorithm to remove the redundant waypoints of the path returned by RRT*, and, by applying a smoothing technique, our algorithm returns a G2 continuous path that is suitable for semi-autonomous vehicles. In order to deal with sudden changes in the environment, we apply a replanning procedure to enable our algorithm to rapidly react to the changes in a real-time manner, without full recomputation of the RRT* solution. Numerical simulations demonstrate the effectiveness of the proposed method.

Keywords: Autonomous systems, Robotics, Optimization
Abstract: New requirements of autonomous mobile vehicles necessitate hierarchical motion-planning techniques that not only find a plan to satisfy high-level specifications, but also guarantee that this plan is suitable for execution under vehicle dynamical constraints. In this context, the H-cost motion-planning technique has been reported in the recent literature. We propose an incremental motion-planning algorithm based on this technique. The proposed algorithm retains the benefits of the original technique, while significantly reducing the associated computational time. In particular, the proposed iterative algorithm presents during intermediate iterations feasible solutions, with the guarantee that the algorithm eventually converges to an optimal solution. The costs of solutions at intermediate iterations are almost always nonincreasing. Therefore, the proposed algorithm is suitable for real-time implementations, where hard bounds on the available computation time are imposed, and where the original H-cost optimization algorithm may not have sufficient time to converge to a solution at all. We illustrate the proposed algorithm with numerical simulation examples.

Keywords: Stability of nonlinear systems, Optimization algorithms, Autonomous systems
Abstract: In this paper an algorithm is introduced which calculates the region of stability (ROS) of a nonlinear dynamical system. The search for a maximum ROS is based on Lyapunov stability theory, formulated as a constraint optimisation problem. This multimodal problem is solved by a cascaded evolutionary algorithm. R-functions are used to enhance the estimation of contour and size of the ROS.

Keywords: Sliding mode control, Lyapunov methods
Abstract: A novel sliding mode control strategy is presented in this paper for a class of parallel underactuated nonlinear systems, where a single scalar control input simultaneously affects multiple states. A key challenge in the control design for this class of systems is that standard backstepping-based approaches cannot be applied. This difficulty is mitigated through innovative selection of a sliding surface, along with a novel sequential control design procedure. The proposed control method is continuous, and it is designed to me computationally minimal, requiring no observers, function approximators, or online adaptive laws. The proposed control law achieves asymptotic regulation of multiple states using a scalar control input. A rigorous stability analysis is provided to prove the theoretical result, and numerical simulation results based on an inverted pendulum system are provided to demonstrate the effectiveness of the proposed control algorithm.

Keywords: Sliding mode control, Robust control, Electrical power systems
Abstract: In this paper, we propose a robust voltage control scheme for microgrids based on a suitable designed third-order sliding mode (3-SM) controller. The use of 3-SM allows to reject matched disturbances and unmodeled dynamics, due to the presence of a voltage-sourced-converter (VSC) as interface with the main grid. The motivation for using a 3-SM control approach, apart from its property of providing robustness to the scheme in front of a significant class of uncertainties, is also given by its capability of enforcing sliding modes of the controlled system with chattering alleviation. The microgrid system controlled via the proposed 3-SM approach proves to exhibit appreciable stability properties. Specifically, the voltage error with respect to the required reference is steered to zero in a finite time. The comparison with respect to second order sliding mode (SOSM) and PI controllers shows the beneficial effects of the proposed strategy, and simulation results confirm that our control law provides closed-loop performance complying with the IEEE recommendations for power systems.

Keywords: Sliding mode control, Robust control
Abstract: In this work the reaching law approach to the sliding mode control of discrete time systems is considered. We propose a reaching law, in which the convergence of the sliding variable to the vicinity of zero is governed by the inverse tangent function. First we analyze the case of the unperturbed system, and then we consider a second scenario with unknown disturbances and parameter uncertainties. We demonstrate, that for both cases the presented reaching law guarantees the quasi sliding motion of the representative point defined as crossing the sliding hyperplane in each successive control period while remaining inside an a priori known band around the hyperplane. When compared to the most popular, constant plus proportional reaching law, the proposed solution offers better robustness and faster convergence.

Keywords: Sliding mode control, Delay systems, Stability of nonlinear systems
Abstract: A new continuous sliding mode control approach is introduced with the dedicated mathematical tools. A time-delay modification/approximation of sign function is proposed, and it is shown that by substituting this new “sign” realization in the conventional sliding mode algorithms the main advantages of the sliding mode tools are preserved (like time of convergence and uniformity of stability with respect to matched disturbances), while the chattering is seriously reduced. These results are illustrated and confirmed by numerical simulations for the first order sliding mode control and the super-twisting algorithm.

Keywords: Sliding mode control, Stability of nonlinear systems, Lyapunov methods
Abstract: In this work, a dynamic feedback control law based on the well known ``Twisting'' algorithm is under study. Dynamic compensation is added to the Twisting algorithm, in order to use position feedback only, and keep properties such as finite time stability. Moreover, linear terms are considered to improve the robustness of the algorithm. In some mechanical applications, an observer or a differentiator design is required for control purposes when the whole state space is not available for measurement. An alternative solution for this problem is proposed: a finite time stable algorithm that uses dynamic position feedback. Indeed, this new proposal does not require to measure or estimate another signal but the position of the mechanical system. In the stability analysis, strict nonsmooth Lyapunov functions and homogeneity properties are used in order to show finite time stability and robustness. Based on the proposed algorithm, a control law for a one-link pendulum affected by Coulomb friction and bounded external perturbations is designed.

Keywords: Sliding mode control
Abstract: In this paper a step by step adaptive super twisting controller is designed for second order nonlinear strict feedback system with mismatched uncertainty. The design procedure is carried out by considering a virtual controller at each step. After obtaining the first step the desired dynamic model for each state is defined by the previous one and finally the actual control law is obtained. The design parameters of super twisting controller are estimated adaptively. The proposed method can be used for disturbance estimation also. The finite time convergence has been obtained by using a strict Lyapunov function. Simulation results demonstrate the efficacy of the proposed controller.

Keywords: Decentralized control, Distributed control, Power plants
Abstract: This paper deals with control strategies for an Integrated Solar Combined Cycle (ISCC). It first presents a hybrid dynamical model of an ISCC plant, obtained by coupling a Combined Cycle Power Plant with a solar Parabolic Trough reflector. In particular, this model describes the behavior of the ISCC power. This model is used to develop and compare two control strategies on the global power: decentralized control and coordinated control. The coordinated control shows that the solar part can help to reduce the global fuel consumption.

Keywords: Decentralized control, Electrical power systems, Agents networks
Abstract: We propose an algorithm to deal with the problem of decentralized coordination of charging/discharging of a large population of plug-in electric vehicles (PEVs). We introduce a framework in which the power grid is modeled as an undirected rooted tree. The root of the tree represents the generation/transmission side of the system and the leaves represent PEVs. Intermediate nodes represent congestible elements on the distribution side (e.g., transformers), which have a bound on the demand they can attend. In the proposed algorithm, the root generates a control signal based on the price per unit of power according to the demand for each time. Intermediate nodes modify the control signal according to the difference between the demand they take care of, and its capacity upper bound. PEVs update their charging/discharging strategies according to this pricing signal. Simulations demonstrate the algorithm performance for a particular example.

Keywords: Decentralized control, Game theoretical methods, Large-scale systems
Abstract: Control of Urban Drainage Systems (UDS) is studied for cases in which the distribution of run–off through the channels of a system is inefficient, i.e. when the capacity of some structures is not used optimally. A decentralized population dynamics-based control for UDS is presented, particularly using the replicator and projection dynamics. For the design, a methodology to make a partitioning of the system is introduced, and the design of a population dynamics–based control per each partition is proposed. Moreover, a stability analysis of the closed–loop system is made by using passivity theory. Finally, simulation results show the proposed approach performance in a segment of the Bogot´a stormwater UDS case study.

Keywords: Decentralized control, Large-scale systems, Lyapunov methods
Abstract: Based on the Lyapunov stability theorem, a decentralized adaptive backstepping control with perturbation estimation scheme is proposed in this paper for a class of perturbed multi-input large-scale systems to solve regulation problems. The dynamic equations of the plant are firstly transformed into semi-strict feedback form, and then the decentralized controllers are designed by using backstepping control method to achieve the property of asymptotic stability. In addition, perturbation estimation mechanisms are employed so that there is no need to compute the derivatives of the virtual input functions, and the upper bounds of the perturbations as well as perturbation estimation errors are not required to be known in advance either. A numerical example is also given for demonstrating the feasibility of the proposed control scheme.

Keywords: Decentralized control, Robust control, Nonlinear system identification
Abstract: A critical step in the control design of industrial processes is the Control Configuration Selection (CCS), where each actuator is grouped with a set of measurements to be used in the computation of its control action.

Tools for CCS include gramian-based Interaction Measures (IMs), initially defined for linear systems. Since a trending research topic is the derivation of IMs for nonlinear systems, a decision of the designer is therefore the approach to the problem in the linear or nonlinear framework.

For this end, a method is discussed that determines the degree of nonlinearity of a system based on a specially tailored experiment, and thus enables the selection of the correct framework for the analysis.

The novelty is in the estimation of two gramian-based IMs with confidence bounds from the tailored experiment which is applicable if the process is revealed to be weakly nonlinear.

Keywords: Energy systems, Decentralized control, Linear systems
Abstract: The tower contributes a significant share to the overall cost of an offshore wind energy converter. To allow a more cost-effective design and prevent the occurrence of unscheduled maintenance, it is highly desirable to mitigate severe fatigue loading conditions and constantly monitor the dynamic behavior that the tower exhibits during operation. This paper sought to understand whether state-of-the-art control techniques for tower load reduction might be coupled with an online time-frequency analysis of tower vibrations, in order to extend the ongoing research effort in the area of situation control. Therefore, we performed hydro-aero-elastic simulation for a commercial 5MW offshore wind turbine, for two different sets of wind-sea conditions; simulations were iterated for three alternative control scenarios: baseline control, conventional tower control and advanced tower control. Results showed how the proposed method was able to reduce fatigue loads, without severely impairing pitch actuators, as is the case with classic state-of-the-art tower control methods. Furthermore, connecting these already established, but apparently independent fields of research may result in a sounder technical solution, which is better tailored to the technology requirements that offshore wind energy demands.