Abstract: Publication date: Available online 16 February 2019Source: AutomaticaAuthor(s): Antonio Sala This paper discusses a ‘scenario’ approach to prove decay-rate stability of discrete-time polytopic linear parameter-varying systems, dealing with sets of sequences of vertex models of different length. When all sequences have the same length, parameter-trajectory dependent results in earlier literature are obtained as particular cases. The approach in this paper discusses ‘classical’ stability, without the need of probabilistic ingredients present in other scenario-based ideas in literature. A numerical example shows that the proposal achieves a sensible tradeoff between proven performance and computing requirements.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Xiaodi Li, Xueyan Yang, Shiji Song In this paper, we develop the Lyapunov–Razumikhin method to finite-time stability (FTS) and finite-time contractive stability (FTCS) of time-delay systems. Several Lyapunov-based sufficient conditions for establishing these FTS properties are obtained. Then the theoretical results are applied to FTS and FTCS for a class of linear time-varying (LTV) time-delay system. The efficiency of the proposed criteria is illustrated by three numerical examples, where a stabilizing memoryless controller for FTCS of a second-order LTV system with time delay is proposed.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Didier Henrion, Martin Kružík, Tillmann Weisser Optimal control problems with oscillations (chattering controls) and concentrations (impulsive controls) can have integral performance criteria such that concentration of the control signal occurs at a discontinuity of the state signal. Techniques from functional analysis (anisotropic parametrized measures) are applied to give a precise meaning of the integral cost and to allow for the sound application of numerical methods. We show how this can be combined with the Lasserre hierarchy of semidefinite programming relaxations.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Wenshuo Li, Zidong Wang, Qinyuan Liu, Lei Guo The remote state estimation problem is considered for general non-Gaussian systems. The estimator runs particle filtering algorithm to track the non-Gaussian probability density function (PDF) of the target state. We are concerned with the reduction of sensor-to-estimator communication while maintaining acceptable estimation accuracy. For this purpose, a novel event-based transmission scheme is proposed where the Kullback–Leibler divergence is used to identify informative measurements. We develop a two-step approximation procedure to obtain a parametric form for the event generator function, thereby enabling each sensor to quantify the informativeness of its current measurement without running a copy of the estimator. Furthermore, a Monte Carlo method is proposed to evaluate the likelihood function of the set-valued measurements. Simulation results demonstrate the effectiveness of our scheme, especially when the predictive PDF of the measurement is strongly non-Gaussian.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Wenbing Zhang, Qing-Long Han, Yang Tang, Yurong Liu This paper is concerned with the sampled-data control for a class of linear time-varying system. A classic Halanay inequality is first extended to the time-varying sampled-data system. Then based on the comparison principle and the extended Halanay inequality, new criteria for globally uniformly exponential stability and globally uniformly asymptotic stability of the corresponding closed-loop system are derived. Furthermore, an algorithm is presented to solve the gain synthesis problem. Finally, one example is given to show the effectiveness of the obtained results.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Hui Xiao, Hu Chen, Loo Hay Lee This research considers the problem of ranking the top simulated designs in the presence of stochastic constraints. The objective and constraint measures of each design must be estimated via simulation. Given a fixed simulation budget, the ranking of the top feasible designs cannot be determined with certainty. The objective of this research is to derive an efficient simulation budget allocation strategy such that the probability of correct ranking (PCR) can be maximized. To deal with this problem, we propose a lower bound on the PCR and develop an asymptotically optimal allocation rule based on the lower bound. Useful insights on characterizing the allocation rule are provided, and numerical experiments are carried out to demonstrate the efficiency of the suggested simulation procedure.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Tarek Ahmed-Ali, Koen Tiels, Maarten Schoukens, Fouad Giri The problem of sampled-data observer design is addressed for a class of state- and parameter-affine nonlinear systems. The main novelty in this class lies in the fact that the unknown parameters enter the output equation and the associated regressor is nonlinear in the output. Wiener systems belong to this class. The difficulty with this class of systems comes from the fact that output measurements are only available at sampling times causing the loss of the parameter-affine nature of the model (except at the sampling instants). This makes existing adaptive observers inapplicable to this class of systems. In this paper, a new sampled-data adaptive observer is designed for these systems and shown to be exponentially convergent under specific persistent excitation conditions that ensure system observability and identifiability. The new observer involves an inter-sample output predictor that is different from those in existing observers and features continuous trajectories of the state and parameter estimates.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Bin Hu, Zhi-Hong Guan, Minyue Fu This paper is concerned with how multi-agent networks achieve finite-time consensus using distributed event-driven control. Due to the hybrid nonlinearities arising from the nonsmooth control and the triggering condition, finite-time consensus analyses are more challenging with event-driven control than with continuous-time control. We study agents with single integrator dynamics and scalar states and present a distributed event-driven control protocol for the finite-time consensus, with comparison to continuous-time control. It is shown that using the proposed event-driven control scheme, agents can reach consensus within a limited time and without Zeno behavior. We also obtain an estimate for the settling time and demonstrate that it is not only related to the initial condition and network connectivity, but is also linked with the event-triggering condition. Simulations are given to demonstrate the theoretical results.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Alessandro Falsone, Kostas Margellos, Maria Prandini We address the optimization of a large scale multi-agent system where each agent has discrete and/or continuous decision variables that need to be set so as to optimize the sum of linear local cost functions, in presence of linear local and global constraints. The problem reduces to a Mixed Integer Linear Program (MILP) that is here addressed according to a decentralized iterative scheme based on dual decomposition, where each agent determines its decision vector by solving a smaller MILP involving its local cost function and constraint given some dual variable, whereas a central unit enforces the global coupling constraint by updating the dual variable based on the tentative primal solutions of all agents. An appropriate tightening of the coupling constraint through iterations allows to obtain a solution that is feasible for the original MILP. The proposed approach is inspired by a recent paper to the MILP approximate solution via dual decomposition and constraint tightening, but shows finite-time convergence to a feasible solution and provides sharper performance guarantees by means of an adaptive tightening. The two approaches are compared on a plug-in electric vehicles optimal charging problem.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Jonathan P. Epperlein, Sergiy Zhuk, Robert Shorten Situations in which recommender systems are used to augment decision making are becoming prevalent in many application domains. Almost always, these prediction tools (recommenders) are created with a view to affecting behavioural change. Clearly, successful applications actuating behavioural change, affect the original model underpinning the predictor, leading to an inconsistency. This feedback loop is often not considered in standard machine learning techniques which rely upon machine learning/statistical learning machinery. The objective of this paper is to develop tools that recover unbiased user models in the presence of recommenders. More specifically, we assume that we observe a time series which is a trajectory of a Markov chain R modulated by another Markov chain S, i.e. the transition matrix of R is unknown and depends on the current state of S. The transition matrix of the latter is also unknown. In other words, at each time instant, S selects a transition matrix for R within a given set which consists of known and unknown matrices. The state of S, in turn, depends on the current state of R thus introducing a feedback loop. We propose an Expectation–Maximisation (EM) type algorithm, which estimates the transition matrices of S and R. Experimental results are given to demonstrate the efficacy of the approach.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Maor Braksmayer, Leonid Mirkin The paper studies the H2 optimal control for discrete-time systems under the constraint that the information exchange between the sensor- and actuator-side parts of the controller is intermittent, a priori unknown, and independent of the process. A closed-form analytic solution to the problem is derived. The solution is based on two standard algebraic Riccati equations and is readily implementable. An implementation maintaining optimality of the controller under packet losses, which can be modeled by the intermittent communication framework, is also proposed. On the technical side, the paper reveals an issue related to the need for extending the time-varying error system to the whole discrete time axis Z in order to solve the corresponding H2 model-matching problem and proposes a procedure to resolve it.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Zhonghua Miao, Jinwei Yu, Jinchen Ji, Jin Zhou This paper is concerned with the multi-objective region reaching control for a swarm of robots which are formulated by Lagrangian dynamics. Two distributed multi-objective region reaching control protocols are proposed for the networked robotic systems under directed acyclic topology, and a unifying methodology is presented to perform the convergence analysis for the robotic systems with static and moving target regions. The control strategy is developed by using the potential energy function approach, and the specified shapes of the various desired regions are constructed by selecting appropriate objective functions. In this control strategy, a network of a large number of robots evolves into multiple groups, and the robots in each group only require communicating with their neighbors. Thus, the proposed control strategy is effective for multi-objective region reaching control for a swarm of robots in practical applications. Finally, simulation examples are given to show the validity of the theoretical results.,

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Jeremy Coulson, Bahman Gharesifard, Abdol-Reza Mansouri In this paper, we study the average controllability of a random heat equation, with the diffusivity serving as the random variable drawn from a general probability distribution. We show that the solutions of such random heat equations are both null and approximately controllable in average from an arbitrary open set of the domain and in an arbitrarily small time, recovering the known result when the random diffusivity is uniformly or exponentially distributed.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Carlos E. de Souza This paper deals with the problem of robust H∞ filtering for discrete-time systems with Lipschitz nonlinearities and uncertain parameters. Both the cases of systems subject to either polytopic or norm-bounded parameter uncertainties are treated and it is considered that all the matrices of the system state-space model can be affected by uncertain parameters. Novel methods in terms of linear matrix inequalities are proposed for designing nonlinear filters with a general structure that ensure global exponential stability of the estimation error dynamics and a prescribed (or optimized) H∞ performance for all admissible uncertain parameters. In the case of polytopic uncertain systems, the filter design is based on an affine uncertainty-dependent Lyapunov function. Numerical examples are presented to illustrate the effectiveness of the proposed robust H∞ filtering methods.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Salvatore Monaco, Lorenzo Ricciardi Celsi This paper deals with multi-consensus in multi-agent systems, focusing on the relationship between multi-consensusability and the underlying digraph topology. In particular, the topological arrangement of the network nodes naturally suggests distinguishing among, on the one hand, separate independent groups of agents agreeing internally and, on the other hand, a dependent common subgraph whose internal consensuses can be computed as a convex combination of the different consensuses achieved by the previously mentioned independent groups. The distinct achieved consensuses are as many as the number of groups of agents defining cells of a suitable almost equitable partition. Despite the notational complexity, the related computations are quite simple to carry out, as shown in some examples.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Chao Deng, Guang-Hong Yang In this paper, the cooperative output regulation problem for linear multi-agent systems with actuator faults is considered. It is assumed that the actuator faults are outage faults and loss-of-effectiveness faults. First, a distributed finite-time observer is designed to estimate the state of the exosystem. Based on the state of the finite-time observer, a distributed adaptive fault-tolerant controller is designed. Then, it is shown that the cooperative output regulation problem can be solved with the proposed fault-tolerant controller. Compared with the existing cooperative output regulation results, a novel lemma is introduced to guarantee the solvability of the regulator equations under actuator faults, and the developed controller is effective to compensate the actuator faults. Finally, a simulation example is presented to show the validity of the proposed method.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): K.D. Do This paper considers the feedback tracking control problem of drill-heads with the bit–rock interaction modeled by drifts and jump–diffusions (Lévy processes). A Lyapunov-type theorem is developed to study well-posedness, stability in moment, and almost sure stability of nonlinear stochastic differential equations driven by Lévy processes. This theorem and the backstepping method are applied to design robust and adaptive controllers that guarantee both global practical K∞-exponential p-stability and almost sure global practical K∞-exponential stability of the tracking errors for the drill-heads.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Xiaoxu Liu, Zhiwei Gao, Aihua Zhang In this paper, robust fault estimation and fault tolerant control for stochastic Takagi–Sugeno fuzzy systems, subjected to Brownian parameter perturbations, unknown process uncertainties and unexpected faults, are investigated. Augmented system approach, unknown input observer techniques and sliding mode control strategies are integrated to decouple the influences from the unknown input uncertainties, and drive the trajectories of the estimation error dynamics to enter and subsequently remain within a desired surface of the error space. As a result, a robustly simultaneous estimate of the means of the faults concerned and the full system states can be achieved. In the meanwhile, the actuator/sensor signal compensation techniques are used to formulate the tolerant control strategy to eliminate or offset the influences from the faults to the systems dynamics and ensure the robust stabilization of the closed-loop control system. In terms of linear matrix inequalities, sufficient conditions are proposed to ensure the robust stability of the overall closed-loop system composed of system state and estimation error dynamics, as well as the reachability of the sliding mode surface. Furthermore, the systematic design procedures for the robust fault estimation and fault tolerant control scheme are addressed. Finally, simulation studies on a single-link manipulator and a three-tank system are illustrated to demonstrate the effectiveness of the suggested methodologies.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Farzad Salehisadaghiani, Wei Shi, Lacra Pavel In this paper, we consider the problem of finding a Nash equilibrium in a multi-player game over generally connected networks. This model differs from a conventional setting in that players have partial information on the actions of their opponents and the communication graph is not necessarily the same as the players’ cost dependency graph. We develop a relatively fast algorithm within the framework of inexact-ADMM, based on local information exchange between the players. We prove its convergence to Nash equilibrium for fixed step-sizes and analyse its convergence rate. Numerical simulations illustrate its benefits when compared to a consensus-based gradient type algorithm with diminishing step-sizes.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Ahmad Ansari, Dennis S. Bernstein This paper considers discrete-time input reconstruction and state estimation assuming that the system has no invariant zeros, without assuming that the initial condition is known, and without assuming that at least one Markov parameter has full column rank. Algorithms based on the generalized inverse of a block-Toeplitz matrix are given for unknown-input state estimation and simultaneous input reconstruction and state estimation. In both cases, the unknown input is an arbitrary signal. Both algorithms are deadbeat, which means that exact input reconstruction and state estimation are achieved in a finite number of steps.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Kiran Kumari, Bijnan Bandyopadhyay, Kyung-Soo Kim, Hyungbo Shim In this paper, we present an output feedback based design of event-triggered sliding mode control for delta operator systems. For discrete-time systems, multi-rate output sampling based state estimation technique is very useful if the output information is available. But at high sampling rates, the discrete-time representation of the system using shift operator becomes numerically ill-conditioned and as a result, the observability matrix becomes singular as the sampling period tends to zero. Here, a new formulation of multi-rate state estimation (MRSE) for a small sampling period is presented. We first propose a new observability matrix and then discuss its relationship with the observability matrix defined in the conventional sense. For the delta operator system with matched uncertainty, we have presented the design of MRSE based sliding mode control (SMC). Additionally, to make the control efficient in terms of resource utilization, MRSE based event-triggered SMC is proposed. The absence of Zeno phenomenon is guaranteed as the control input is inherently discrete in nature. Finally, the effectiveness of the proposed method is illustrated through numerical simulations, considering a ball and beam system and a general linear system as a numerical example.

Abstract: Publication date: May 2019Source: Automatica, Volume 103Author(s): Xianlin Zeng, Jie Chen, Shu Liang, Yiguang Hong This paper investigates the distributed strategy design to find generalized Nash equilibria (GNE) of multi-cluster games with nonsmooth payoff functions, a coupled nonlinear inequality constraint, and set constraints. In this game, each cluster is composed of a group of agents and is a virtual noncooperative player, who minimizes its payoff function; each agent only uses its local payoff function, local feasible set and partial information of the coupled inequality constraint, and communicates with its neighbors. To solve the GNE problem, we propose a distributed nonsmooth algorithm using a projected differential inclusion and establish the convergence analysis of the proposed algorithm. A numerical application is given for illustration.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Ruohan Yang, Hao Zhang, Gang Feng, Huaicheng Yan, Zhuping Wang This paper investigates the robust cooperative output regulation problem of uncertain linear multi-agent systems with additive disturbances via the celebrated internal model principle. Two novel distributed controllers are designed based on the adaptive control strategy and the event-triggered transmission scheme, where the adaptive control strategy is utilized to avoid the requirement for a priori knowledge of the minimal nonzero eigenvalue of the Laplacian matrix associated with the communication topology, and the event-triggered transmission scheme is utilized to reduce the frequency of data transmission. It is shown that the proposed control schemes achieve robust cooperative output regulation asymptotically and Zeno behavior is excluded. Finally, a simulation example is presented to illustrate the effectiveness of the proposed controllers.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Jinxing Zhang, Jiandong Zhu For the Kuramoto model and its variations, it is difficult to analyze the exponential synchronization under the general digraphs due to the lack of symmetry. In this paper, for the high-dimensional Kuramoto model of identical oscillators, a matrix Riccati differential equation (MRDE) is proposed to describe the error dynamics. Based on the MRDE, the exponential synchronization is proved by constructing a total error function for the case of digraphs admitting directed spanning trees.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Peng Yi, Lacra Pavel In this paper, we propose a distributed algorithm for computation of a generalized Nash equilibrium (GNE) in noncooperative games over networks. We consider games in which the feasible decision sets of all players are coupled together by a globally shared affine constraint. Adopting the variational GNE as a refined solution, we reformulate the problem as that of finding the zeros of a sum of monotone operators through a primal–dual analysis and an augmentation of variables. Then we introduce a distributed algorithm based on forward–backward operator splitting methods. Each player only needs to know its local objective function, local feasible set, and a local block of the affine constraint, and share information with its neighbours. However, each player also needs to observe the decisions that its objective function directly depends on to evaluate its local gradient. We show convergence of the proposed algorithm for fixed step-sizes under some mild assumptions. Moreover, a distributed algorithm with inertia is also introduced and analysed for distributed variational GNE seeking. Numerical simulations are given for networked Cournot competition with bounded market capacities, to illustrate the algorithm efficiency and performance.

Abstract: Publication date: Available online 4 February 2019Source: AutomaticaAuthor(s): Laura Menini, Corrado Possieri, Antonio Tornambe Three dynamical algorithms are proposed to invert maps that depend on a measurable piecewise constant exogenous signal (i.e., switched maps). In particular, under mild assumptions on the switching signal, it is shown that switched maps can be inverted asymptotically, in finite time, and practically by the respective algorithms.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Huifang Min, Shengyuan Xu, Baoyong Zhang, Qian Ma This paper addresses the globally adaptive state-feedback control problem for a more general class of stochastic nonlinear systems with an unknown time-varying delay and perturbations. Without imposing any assumptions on the time-varying delay, an adaptive state-feedback controller is skillfully designed by using adaptive backstepping control technique. Then, based on Lyapunov–Razumikhin lemma and stochastic stability theory, it is proven that the constructed controller can guarantee the closed-loop system to be globally asymptotically stable in probability. Finally, a practical example of stochastic chemical reactor system with time delay and perturbations is presented to demonstrate the effectiveness of the proposed control scheme.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Hyeonjun Yun, Hyungbo Shim, Hyo-Sung Ahn This paper considers the economic dispatch problem for a network of power generators and customers. In particular, our aim is to minimize the total generation cost under the power supply–demand balance and the individual generation capacity constraints. This problem is solved in a distributed manner, i.e., a dual gradient-based continuous-time distributed algorithm is proposed in which only a single dual variable is communicated with the neighbors and no private information of the node is disclosed. The proposed algorithm is simple and no specific initialization is necessary, and this in turn allows on-line change of network structure, demand, generation constraints, and even the participating nodes. The algorithm also exhibits a special behavior when the problem becomes infeasible so that each node can detect over-demand or under-demand situation of the power network. Simulation results on IEEE 118 bus system confirm robustness against variations in power grids.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Van Sy Mai, Eyad H. Abed This paper deals with an optimization problem over a network of agents, where the cost function is the sum of the individual (possibly nonsmooth) objectives of the agents and the constraint set is the intersection of local constraints. Most existing methods employing subgradient and consensus steps for solving this problem require the weight matrix associated with the network to be column stochastic or even doubly stochastic, conditions that can be hard to arrange in directed networks. Moreover, known convergence analyses for distributed subgradient methods vary depending on whether the problem is unconstrained or constrained, and whether the local constraint sets are identical or nonidentical and compact. The main goals of this paper are: (i) removing the common column stochasticity requirement; (ii) relaxing the compactness assumption, and (iii) providing a unified convergence analysis. Specifically, assuming the communication graph to be fixed and strongly connected and the weight matrix to (only) be row stochastic, a distributed projected subgradient algorithm and a variation of this algorithm are presented to solve the problem for cost functions that are convex and Lipschitz continuous. The key component of the algorithms is to adjust the subgradient of each agent by an estimate of its corresponding entry in the normalized left Perron eigenvector of the weight matrix. These estimates are obtained locally from an augmented consensus iteration using the same row stochastic weight matrix and requiring very limited global information about the network. Moreover, based on a regularity assumption on the local constraint sets, a unified analysis is given that can be applied to both unconstrained and constrained problems and without assuming compactness of the constraint sets or an interior point in their intersection. Further, we also establish an upper bound on the absolute objective error evaluated at each agent’s available local estimate under a nonincreasing step size sequence. This bound allows us to analyze the convergence rate of both algorithms.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Nipun Popli, Sérgio Pequito, Soummya Kar, A. Pedro Aguiar, Marija Ilić This paper addresses the problem of minimum-cost resilient actuation–sensing–communication co-design for regular descriptor systems ensuring selective (i.e., under uncertain zero, non-zero and possible zero parameters) strong structural system’s properties. Towards this goal, we extend the strong structural systems’ theory to cope with the selective structure that captures resiliency properties and uncertainty properties of regular descriptor system’s model. We provide necessary and sufficient conditions for this property to hold and show how these conditions can be leveraged to determine the minimum-cost resilient placement of actuation–sensing–communication technology ensuring feasible solutions. Finally, we illustrate the applicability of the main findings on an electric power grid example.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Li Liang, Fang Deng, Zhihong Peng, Xinxing Li, Wenzhong Zha Multi-player pursuit–evasion games are crucial for addressing the maneuver decision problem arising in the cooperative control of multi-agent systems. This work addresses a particular pursuit–evasion game with three players, Target, Attacker, and Defender. The Attacker aims to capture the Target, while avoiding being captured by the Defender and the Defender tries to defend the Target from being captured by the Attacker, while trying to capture the Attacker at an opportune moment. A two-pronged pursuit–evasion problem in this game is considered and we focus on two aspects: the cooperation between the Target and Defender and balancing the roles of the Attacker between pursuer and evader. A barrier based on the explicit policy method and geometric analysis method is constructed to separate the whole state space into two disjoint parts that correspond to two winning regions for the Attacker and Target–Defender team. The main contributions of this work are obtaining the players’ winning regions and providing a complete game solution by analyzing the optimal strategies and trajectories of the players based on the barrier.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Miguel Galrinho, Cristian R. Rojas, Håkan Hjalmarsson Standard system identification methods often provide inconsistent estimates with closed-loop data. With the prediction error method (PEM), this issue is solved by using a noise model that is flexible enough to capture the noise spectrum. However, a too flexible noise model (i.e., too many parameters) increases the model complexity, which can cause additional numerical problems for PEM. In this paper, we consider the weighted null-space fitting (WNSF) method. With this method, the system is first modeled using a non-parametric ARX model, which is then reduced to a parametric model of interest using weighted least squares. In the reduction step, a parametric noise model does not need to be estimated if it is not of interest. Because the flexibility of the noise model is increased with the sample size, this will still provide consistent estimates in closed loop and asymptotically efficient estimates in open loop. In this paper, we prove these results, and we derive the asymptotic covariance for the estimation error obtained in closed loop, which is optimal for an infinite-order noise model. For this purpose, we also derive a new technical result for geometric variance analysis, instrumental to our end. Finally, we perform a simulation study to illustrate the benefits of the method when the noise model cannot be parametrized by a low-order model.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Zhiyong Chen Nussbaum functions have been successfully used in adaptive controller design for dealing with unknown control direction since the original work in 1983. However, for time-varying control coefficients of an unknown sign (positive or negative), only a special Nussbaum function can be proved to be effective based on the explicit calculation on the particular function. It remains open whether a general Nussbaum function is sufficient in these scenarios and why. This paper gives a No answer with a counter example. Moreover, it introduces new types of Nussbaum functions and reveals their fundamental characteristics that are sufficient for dealing with time-varying unknown control coefficients in adaptive control. A multivariable version is also introduced.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Wen Yang, Yu Zhang, Guanrong Chen, Chao Yang, Ling Shi We consider the problem of network security for distributed filtering under false data injection attacks over a wireless sensor network. To resist the hostile attacks from a malicious attacker who can inject false data into communication channels according to a certain probability, we design a protector for each sensor based on the online innovation information from its neighboring sensors to decide whether to use the received data at each time. To guarantee the Gaussianity of the innovations, we use a stochastic rule to transform the threshold detection. We also provide a sufficient condition for the stability of the estimator equipped with the proposed protector under hostile attacks. Moreover, we find a critical attack probability above which the steady-state estimation error covariance will exceed a pre-set value. Finally, we compare the estimation performances among several protection strategies, and explore the relationship between the system parameters and the protection effect.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Bomin Jiang, Zhiyong Sun, Brian D.O. Anderson, Christian Lageman Most current results on coverage control using mobile sensors require that one partitioned cell is associated with precisely one sensor. In this paper, we consider a class of coverage control problems involving higher order Voronoi partitions, motivated by applications where more than one sensor is required to monitor and cover one cell. Such applications are frequent in scenarios requiring the sensors to localize targets. We introduce a framework depending on a coverage performance function incorporating higher order Voronoi cells and then design a gradient-based controller which allows the multi-sensor system to achieve a local equilibrium in a distributed manner. The convergence properties are studied and related to Lloyd algorithm. We study also the extension to coverage of a discrete set of points and its applications to clustering of discrete sets.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Ci Chen, Frank L. Lewis, Shengli Xie, Hamidreza Modares, Zhi Liu, Shan Zuo, Ali Davoudi Resilience of multi-agent systems (MAS) reflects their capability to maintain normal operation, at a prescribed level in the presence of unintended faults. In this paper, we investigate resilient control of MAS under faults on sensors and actuators. We propose four resilient state feedback based leader–follower tracking protocols. For the case of sensor faults, we develop an adaptive compensation protocol and an H∞ control protocol. For the case of simultaneous sensor and actuator faults, we further propose an enhanced adaptive compensation protocol and an enhanced H∞ control protocol. We show the duality between the adaptive compensation protocols and the H∞ control protocols. For adaptive compensation protocols, faults on sensors and actuators are rejected by using local adaptive sensor and actuator compensators, respectively. Moreover, by employing a static output-feedback design technique, we propose H∞ control protocols that guarantee bounded L2 gains of certain errors in terms of the L2 norms of fault signals. This further allows us to prove resilience even if sensor faults are unbounded. Finally, simulation studies validate the effectiveness of the proposed protocols.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Shiyu Zhao, Fabio Pasqualetti This paper aims to establish explicit relationships between the controllability degree of a network, that is, the control energy required to move the network between different states, and its graphical structure and edge weights. As it is extremely challenging to accomplish this task for general networks, we focus on the case where the network controllability Gramian is a diagonal matrix. The main technical contributions of the paper are (i) to derive necessary and sufficient graphical conditions for networks to feature a diagonal controllability Gramian, and (ii) to propose a constructive algorithm to design network topologies and weights so as to generate stable and controllable networks with pre-specified diagonal Gramians. The proposed network design algorithm allows for individual assignment of how each node responds to external stimuli, so as to selectively enforce robustness to external disturbances. While relying on the simplifying assumption of a diagonal controllability Gramian, our analysis reveals novel and counterintuitive controllability properties of complex networks. For instance, we identify a class of continuous-time networks where the control energy is independent of their cardinality and number of control nodes (thus disproving existing results based on numerical controllability studies), discuss their stability margin, and show that the energy required to control a node can be made independent of its graphical distance from the control nodes. These results complement and formally support, or challenge, a series of conjectures based on numerical studies in the field of complex networks.

Abstract: Publication date: April 2019Source: Automatica, Volume 102Author(s): Johannes Köhler, Matthias A. Müller, Frank Allgöwer We propose a novel model predictive control (MPC) formulation, that ensures recursive feasibility, stability and performance under inexact dual optimization. Dual optimization algorithms offer a scalable solution and can thus be applied to large distributed systems. Due to constraints on communication or limited computational power, most real-time applications of MPC have to deal with inexact minimization. We propose a modified optimization problem inspired by robust MPC which offers theoretical guarantees despite inexact dual minimization. The approach is not tied to any particular optimization algorithm, but assumes that the feasible optimization problem can be solved with a bounded suboptimality and constraint violation. In combination with a distributed dual gradient method, we obtain a priori upper bounds on the number of required online iterations. The design and practicality of this method are demonstrated with a benchmark numerical example.