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Linear systems over the max-plus algebra provide a suitable formalism to model discrete event systems where synchronization, without competition, is involved. In this paper, we consider a formulation of the model matching problem for systems of such class, in which the output of a given system, called the plant, is forced, by a suitable input, to track exactly that of a given model. A necessary and sufficient condition for its solvability is obtained by making a suitable use of geometric methods in the framework of systems over the max-plus algebra.

This article suggests a collection of model-based and model-free output-feedback optimal solutions to a general ${H_{∞}}$ control design criterion of a continuous-time linear system. The goal is to obtain a static output-feedback controller while the design criterion is formulated with an exponential term, divergent or convergent, depending on the designer's choice. Two offline policy-iteration algorithms are presented first, which form the foundations for a family of online off-policy designs. These algorithms cover all different cases of partial or complete model knowledge and provide the designer with a collection of design alternatives. It is shown that such a design for partial model knowledge can reduce the number of unknown matrices to be solved online. In particular, if the disturbance input matrix of the model is given, off-policy learning can be done with no disturbance excitation. This alternative is useful in situations where a measurable disturbance is not available in the learning phase. The utility of these design procedures is demonstrated for the case of an optimal lane tracking controller of an automated car.

The distributed observer problem is motivated by the case where the output information of the system is decentralized in different subsystems. In this scene, all the subsystems form an observer network, and each of them has access to only a part of output information and the information exchanged via the given communication networks. Due to the limitation of communication conditions among subsystems, the communication network is often time varying and disconnected. However, the existing research about the aforementioned scene is still not enough to solve this problem. To this end, this article is concerned with the challenge of the distributed observer design for linear systems under time-variant disconnected communication networks. The design method is successfully established by fixing both completely decentralized output information and incompletely decentralized output information into account. Our work overcomes the limitation of the existing results that the distributed observer can only reconstruct the full states of the underlying systems by means of fast switching. In the case of completely decentralized output information, a group of sufficient conditions is put forward for the system matrix, and it is proved that the asymptotical omniscience of the distributed observer could be achieved as long as anyone of the developed conditions is satisfied. Furthermore, unlike similar problems in multiagent systems, the systems that can meet the proposed conditions are not only stable and marginally stable systems but also some unstable systems. As for the case where the output information is not completely decentralized, the results show with the observable decomposition and states reorganization technology that the distributed observer could achieve omniscience asymptotically without any constraints on the system matrix. The validity of the proposed design method is emphasized in two numerical simulations.

In robust control under state constraints the set of admissible inputs is usually considered as given, under the assumption that the actuators have been already designed. However, if the input set is too small any controller will fail in stabilizing the closed-loop system while satisfying all prescribed constraints for some initial states of interest, or vice versa the chosen actuators may be over-sized. To handle this issue, in this paper we address the problem of computing the smallest input constraint set such that the closed-loop system is stabilizable from a prescribed set of initial states while respecting all constraints. We focus our attention on linear systems with additive disturbances, and develop the algorithm based on recursive feasibility of robust model predictive control. We demonstrate the results using numerical examples, in which we consider different metrics for the input constraint set selection.

This paper addresses the approximate and exact controllability issues of switched infinite dimensional linear systems. Firstly, a controllability map that is related to switching sequence and a controllability Gramian operator are proposed, based on which two necessary and sufficient conditions are established respectively for approximate and exact controllability; Secondly, it shows that the obtained conditions can degenerate into the existing controllability criteria of infinite dimensional linear systems, switched finite dimensional linear systems and single finite dimensional linear system; Finally, the new results are applied to analyze the approximate controllability of switched linear parabolic systems and the exact controllability of switched linear wave systems. Two examples are given to illustrate the effectiveness of the proposed approaches.

This paper considers remote state estimation in cyber-physical systems (CPSs) with multiple sensors. Each plant is modeled by a discrete-time stochastic linear system with measurements of each sensor transmitted to the corresponding remote estimator over a shared communication network when their securities are interdependent due to network induced risks. A dynamic nonzero-sum game with asymmetric information is formulated in which each sensor subject to a resource budget constraint needs to decide whether to invest in security for sending data packets, taking the behaviors of other sensors into account. To overcome the difficulty in characterizing or computing the Nash equilibria (NE), the game with asymmetric information is transformed into another game with symmetric information such that the equilibrium of the original game can be obtained by solving the equilibrium of the new game. Under certain conditions, we devise a backward induction algorithm to obtain a subclass of NE of the original game, known as common information based Markov perfect equilibria (CIBMPE). Finally, a numerical example is provided to illustrate the results obtained.

We investigate the implementation of a sparsity promoting hands-off control scheme using the alternating direction method of multiplies (ADMM). In order to minimize the numerical control effort along with the actuation one, only a single ADMM iteration per time step is considered. We analyze the resulting closed-loop dynamics for linear systems with additive disturbances and show that the proposed scheme provides sparse controls as desired and, in addition, ensures input-to-state practical stability. We further point out a close relation between the proposed real-time scheme and (non-minimum time) dead-beat control. Finally, characteristics and effectiveness of the novel hands-off controller(s) are illustrated with numerical examples.

In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system. A critic learning method is used to obtain the desired admittance parameters based on the cost function composed of interaction force and trajectory tracking without the knowledge of the environmental dynamics. To deal with dynamic uncertainties in the control system, a neural-network (NN)-based adaptive controller with a dynamic learning framework is developed to guarantee the trajectory tracking performance. Experiments are conducted and the results have verified the effectiveness of the proposed method.

Saturation phenomena often exist due to limited system resources, and impulsive protocols can lead to a reduction in communication cost. From these issues, this article investigates a leader-based formation control problem of multiagent systems via asynchronous impulsive protocols with saturated feedback. General linear system models with and without finite time-varying time delays under asymmetric saturated feedback control are concurrently considered. The asynchronous impulsive protocols only permit communication at impulsive instants and each agent has its own communication instants independently. Moreover, to improve system performance, an offset only containing desired formation information is introduced. Finally, because the feedbacks are saturated, admissible regions are proved to exist, which are also estimated by a mean of optimization. Numerical simulations are presented to demonstrate the validity of the proposed schemes.

We investigate state estimation of linear systems over channels having a finite state not known by the transmitter or receiver. We show that similar to memoryless channels, zero-error capacity is the right figure of merit for achieving bounded estimation errors. We then consider finite-state, worst-case versions of the common erasure and additive noise channels models, in which the noise is governed by a finite-state machine without any statistical structure. Upper and lower bounds on their zero-error capacities are derived, revealing a connection with the topological entropy of the channel dynamics. Separate necessary and sufficient conditions for bounded linear state estimation errors via such channels are obtained. These estimation conditions bring together the topological entropies of the linear system and the discrete channel.

In this paper, a noisy output-based direct learning tracking control is proposed for stochastic linear systems with nonuniform trial lengths. The iteration-varying trial length is modeled using a Markov chain for demonstration of the iteration-dependence. The effect of the noisy output is asymptotically eliminated using a prior given decreasing gain sequence in the learning algorithm. Two alternative adaptive gains are presented for improving the tracking performance and the convergence speed. Both the mean square and almost sure convergence are provided. Numerical simulations on a 4 DoF robot arm are presented to illustrate the effectiveness of the proposed scheme.

In a recent paper, we have shown how to learn controllers for unknown linear systems using finite length noisy data by solving linear matrix inequalities. In this note, we extend this approach to deal with unknown nonlinear polynomial systems by formulating stability certificates in the form of data-dependent sum of squares programs, whose solution directly provides a stabilizing controller and a Lyapunov function. We then derive variations of this result that lead to more advantageous controller designs. The results also reveal connections to the problem of designing a controller starting from a least-square estimate of the polynomial system.

Autonomous Robots with multiple directional thrusters are normally over-actuated systems that require nonlinear control allocation methods to map the forces that drive the robot’s dynamics and act as virtual control variables to the actuators. This process demands computational efforts that, sometimes, are not available in small robotic platforms. The present paper introduces a new control allocation approach with fast convergence, high accuracy, and dealing with complex nonlinear problems, especially in embedded systems. The adopted approach divides the desired nonlinear system into coupled linear problems. For that purpose, the Real Actions (RAs) and Virtual Control Variables (VCVs) are broke in two or more sets each. While the RA subsets are designed to linearize the system according to different input subspaces, the VCV is designed to be partially coupled to overlap the output subspaces. This approach generates smaller linear systems with fast and robust convergence used sequentially to solve nonlinear allocation problems. This methodology is assessed in mathematical tutorial cases and over-actuated UAV simulations.

This paper finds the minimum cost that is needed to achieve the approximate controllability of switched infinite-dimensional linear systems (IDLSs) by a newly defined Gramian operator. Such a new cost evaluation method helps to establish a fault recoverability (FR) condition which reveals the ability of faulty switched IDLSs to maintain the approximate controllability under given energetic constraints. An example of the swarming system is taken to illustrate the theoretical results.

This paper investigates the positive consensus problem of multi-agent systems with directed communication topologies where all the agents have identical continuous-time positive linear dynamics. Existing works of such problem mainly focus on the case where the networked communication topologies are of either undirected and incomplete graphs, or strongly connected directed graphs. In stark contrast to them, we study the problem in which the communication topologies of the networked system are described by directed graphs each containing a spanning tree which is a more general and new scenario due to the interplay between the eigenvalues of the Laplacian matrix and the controller gains. Based on the existing results in spectral graph theory and positive linear systems theory, several necessary and sufficient conditions on the positive consensus of the directed multi-agent system are derived through using linear matrix inequality techniques. Then a primal-dual iterative algorithm is developed for computation and finally several numerical simulations are provided to illustrate the effectiveness of the proposed theoretical results.

This article proposes a novel attack detection approach based on zonotopes for linear parameter-varying (LPV) systems with unknown-but-bounded (UBB) noises. The following three types of attacks are considered: 1) denial-of-service (DoS) attacks; 2) replay attacks (RAs); and 3) false-data-injection (FDI) attacks. In order to reduce the conservativeness, a free-weighting matrix is introduced, which can be computed by solving an optimization problem. Moreover, the radius of the intersection zonotope can be guaranteed to be limited as well. Furthermore, it is not necessary to acquire the knowledge about the specific type of attack in advance. Finally, a numerical example is given to illustrate the validity of the given method.

This paper aims to reveal an interesting phenomenon about the time delays in control systems: stability preservation with variable time delays over large intermittently or unbounded. That is, given a system asymptotically stable for bounded time delays, it could remain stable as the time delays are enhanced to have peak values over large intermittently or unbounded. The investigation is carried out by theoretical analysis plus case studies with the stochastic system models. The stability of systems is the core issue, the explicit involvement of the time delays in the stability criteria is the premise for them being concerned. To these ends, a series of lemmas are established preliminarily, a new type stability theorem with its practical version are established in succession, a stability criterion with the time delays as explicit parameters is induced. The Lyapunov function method is applied for less restriction and conservativeness. The mechanism and approach for the emergence of the phenomenon under probe are analyzed. Implementation is considered for the linear system model. Typical variable time delays are constructed for illustrations. Finally, the theoretical

International audience; This paper presents a computational method to test the stabilizability of discrete-time switched linear systems. The existence of a conic cover of the space on whose elements a convex condition holds is proved to be necessary and sufficient for stabilizability. An algorithm for computing a conic partition that satisfies the new necessary and sufficient condition is given. The algorithm, that allows also to determine bounds on the exponential convergence rate, is proved to overcome the conservatism of conditions equivalent to periodic stabilizability and is applied to a four dimensional system.

Reservoir computing has emerged as a powerful machine learning paradigm for harvesting nontrivial information processing out of disordered physical systems driven by sequential inputs. To this end, the system observables must become nonlinear functions of the input history. We show that encoding the input to quantum or classical fluctuations of a network of interacting harmonic oscillators can lead to a high performance comparable to that of a standard echo state network in several nonlinear benchmark tasks. This equivalence in performance holds even with a linear Hamiltonian and a readout linear in the system observables. Furthermore, we find that the performance of the network of harmonic oscillators in nonlinear tasks is robust to errors both in input and reservoir observables caused by external noise. For any reservoir computing system with a linear readout, the magnitude of trained weights can either amplify or suppress noise added to reservoir observables. We use this general result to explain why the oscillators are robust to noise and why having precise control over reservoir memory is important for noise robustness in general. Our results pave the way toward reservoir computing harnessing fluctuations in disordered linear systems.

This paper addresses the problem of estimating the domain of attraction of positive systems under a saturated linear feedback. Compared with the analysis of general saturated linear systems, one remarkable difference is that only the domain of attraction inside the first orthant is of interest. Hence, we tackle this problem by exploiting different shapes of invariant sets confined in the first orthant, including hyper-pyramids and hyper-rectangles, which are, respectively, the level sets of sum-separable and max-separable Lyapunov functions. For an open-loop positive system, a sufficient condition is given to ensure that a hyper-pyramid (or a hyper-rectangle) is contractively invariant for the closed loop. This condition is non-conservative for the single-input case. Moreover, for a system without open-loop positivity, we establish conditions such that the closed loop has a hyper-rectangular invariant set. Examples show the superiority of the results, in comparison with those existing ones using invariant ellipsoids.

A finite-time observer for a class of linear time-varying systems with bounded unknown inputs is presented in this note. The design of the observer exploits structural properties of the system and, through a linear operator, allows applying the robust-exact differentiator in a block form without requiring additional stabilizing terms. The proposed observer provides an exact estimate of the states after a finite transient-time despite the possible instability of the system and the effects of bounded unknown inputs.

In this paper, we propose a dual set membership filter for nonlinear dynamic systems with unknown but bounded noises, and it has three distinctive properties. Firstly, the nonlinear system is translated into the linear system by leveraging a semi-infinite programming, rather than linearizing the nonlinear function. In fact, the semi-infinite programming is to find an ellipsoid bounding the nonlinear transformation of an ellipsoid, which aims to compute a tight ellipsoid to cover the state. Secondly, the duality result of the semi-infinite programming is derived by a rigorous analysis, then a first order Frank-Wolfe method is developed to efficiently solve it with a lower computation complexity. Thirdly, the proposed filter can take advantage of the linear set membership filter framework and can work on-line without solving the semidefinite programming problem. Furthermore, we apply the dual set membership filter to a typical scenario of mobile robot localization. Finally, two illustrative examples in the simulations show the advantages and effectiveness of the dual set membership filter., 26 pages, 9 figures

This article deals with the distributed H∞ robust control problem for linear multiagent systems perturbed by external disturbances and norm-bounded uncertain parameters over the Markovian randomly switching communication topologies. To tackle this problem, the distributed observer-based controller is proposed, which requires the relative information between neighbors and the absolute information of a subset of the nodes, and thus is intrinsically distributed. It is of great interest to see that the distributed H∞ robust control problem governed by such a controller can be converted to the stabilization with H∞ disturbance attenuation problems of some decoupled linear systems, whose dimensions equal those of a single node. Then, the H∞ stochastic robust performance region is defined to indicate the robustness of this controller against the variation of communication topologies. It is theoretically shown that the distributed observer-based controller yields bounded and connected robust performance region. Finally, the theoretical results are verified by conducting numerical simulations and experiments.

A new time-varying adaptive dynamic programming (ADP) algorithm is developed for finite-horizon optimal control problems for a class of discrete-time affine nonlinear systems in this paper. Inspired by the pseudo-linear method, the nonlinear system can be approximated by a series of time-varying linear systems. In each iteration of the time-varying ADP algorithm, the optimal control law for the time-varying linear system is obtained. Convergence analysis is provided for the time-varying ADP algorithm. For an arbitrary initial state, it is proven that states of the time-varying linear systems converge to the states of discrete-time affine nonlinear systems if the convergence criterion is satisfied. It is shown that the iterative value functions and the iterative control laws converge to the optimal value function and the optimal control law, respectively. Finally, numerical results are presented to verify the effectiveness of the present method.

Using the notion of exponential QSR-dissipativity, this work presents necessary and sufficient conditions for exponential stabilizability of nonlinear systems by linear static output feedback (SOF). It is shown that, under mild assumptions, the exponential stabilization of the closed-loop system around the origin is equivalent to the exponential QSR-dissipativity of the plant. Furthermore, whereas strict QSR-dissipativity is only sufficient for SOF asymptotic stabilization, it is proved to be necessary and sufficient for full state feedback control. New necessary and sufficient conditions for SOF stabilizability of linear systems are presented as well, along with a linear and noniterative semidefinite strategy for controller design. Necessary linear matrix inequality (LMI) conditions for stabilization are also introduced. Some examples illustrate the usefulness of the proposed approach.

We address the problem of synthesizing provably correct controllers for linear systems with reach-avoid specifications. Our solution decomposes the overall synthesis problem into two smaller, and more tractable problems: one synthesis problem for an open-loop controller which can produce a reference trajectory, and a second for synthesizing a tracking controller, which can enforce the other trajectories to follow the reference trajectory. As a key building-block result, we show that these two controllers can be synthesized independently. Moreover, we are able to reduce the problem of synthesizing open-loop controllers to satisfiability problems over quantifier-free linear real arithmetic, with the number of constraints linear to the number of hyperplanes as the surfaces of the polytopic obstacles and goal sets. The overall synthesis algorithm computes a tracking controller, and then iteratively covers the entire initial set to find open-loop controllers for initial neighborhoods. The algorithm is sound and, for a class of robust systems, is also complete. We implement this synthesis algorithm in a tool and show promising results on several benchmarks with up to 20

The event-triggered control problem for stochastic nonlinear systems is addressed in the presence of stochastic transmission protocols. A stochastic event-triggered transmission strategy is firstly proposed, which implies that the triggering condition is detected at stochastically distributed detecting instants. The entire system is modeled as a stochastic hybrid system and a new Lyapunov function is constructed to analyze stability of the resulting system. Stability conditions are derived and explicit design of the parameters is also presented. Examples of linear systems and a nonlinear system are provided to illustrate the effectiveness.

In this article, a distributed stochastic model predictive control (MPC) algorithm with a multirate sampling mechanism is proposed for a networked linear system with multiple dynamic subsystems. A delta operator approach is used for the multiple dynamic subsystems with different sampling periods in the multirate sampling mechanism. Both stochastic disturbances and probabilistic constraints of the multiple dynamic subsystems are satisfied by the distributed stochastic MPC algorithm. Packets dropout are considered by the stochastic MPC algorithm and state predicted errors are compensated. Recursive feasibility and quadratic stability are obtained for the networked linear system under the distributed stochastic MPC algorithm. A numerical example is given to illustrate effectiveness of the proposed algorithm.

International audience; In this paper a robust nonlinear Model Reference Adaptive Control (MRAC) is proposed for disturbed linear systems, i.e., linear systems with parameter uncertainties, and external time-dependent perturbations or nonlinear unmodeled dynamics matched with the control input. The proposed nonlinear control law is composed of two nonlinear adaptive gains. Such adaptive gains allow the control to counteract the effects of some perturbations and nonlinear unmodeled dynamics ensuring asymptotic convergence of the tracking error to zero, and the boundedness of the adaptive gains. The nonlinear controller synthesis is given by a constructive method based on the solution of Linear Matrix Inequalities (LMIs). Besides, the simulation results show that, due to the nonlinearities, the rate of convergence of the proposed algorithm is faster than the provided by a classic MRAC.

This article investigates the global stabilization problem of the circular orbit rendezvous system with actuator saturation and time-delay. By decomposing the linearized relative motion equations into a cascade of neutral stable linear systems, linear state feedback controllers are proposed in the presence of both actuator saturation and/or time-delay. The global stability of the closed-loop system is proved. Optimal feedback gain is also obtained in the delay-free case. Simulation results are given to show the effectiveness of the presented methods.

The longitudinal control of the platoon of connected and automated vehicles (CAVs) has gained extensive attention in recent transportation research. A majority of existing results are based on linearized third-order vehicular models, under the premise that a complete priori knowledge of vehicle dynamics is available. This article focuses on a general class of third-order nonlinear CAVs with parametric uncertainty and unknown external disturbance which cannot be linearized. A vehicle-to-vehicle (V2V) communication-based cooperative adaptive backstepping control scheme is proposed, in which unknown parameters and disturbance bounds are estimated on-line. Since the transfer function of linear systems cannot be applied to nonlinear systems to guarantee string stability, asymmetric time-varying constraints are employed to prevent the spacing errors from growing up. A realistic example is considered to verify the feasibility of the control algorithm.

This paper investigates the problem of designing H2 robust (or gain-scheduled) static output-feedback (or state-feedback) controllers for discrete-time positive linear systems affected by time-invariant (or time-varying) parameters belonging to a polytope. For this purpose, an iterative procedure based on robust (parameter-dependent) linear matrix inequalities is proposed. Unlike most approaches, where the controller is obtained by means of a change of variables, the synthesis conditions deal with the control gain directly as an optimization variable, which is specially appealing to cope with closed-loop positivity or structural constraints. The existence of feasible initial conditions for the iterative procedure and some relaxation strategies adopted to reduce the conservativeness of the method are also discussed. Numerical examples borrowed from the literature and statistical comparisons show that the proposed technique is in general less conservative than other approaches, providing solutions and handling cases where traditional design techniques for positive systems cannot be applied.

This article investigates the problem of secure state estimation for continuous-time linear systems in the presence of sparse sensor attacks. Compared with the existing results, the attacked sensor set can be changed by adversaries against secure estimation. To address the more erratic attacks, a novel supervisory state observer is proposed, which employs a bank of candidate nonlinear subobservers and a switching logic administrated by a monitoring function to select the active subobserver at every instant of time. Based on the stability analysis of switched systems, it is proven that the supervisory observer asymptotically converges to a neighborhood of the true system state in the presence of the sensor attacks and bounded disturbances. A simulation example is given to substantiate the theoretical results.

This paper investigates the performance of cubic observers in state estimation of linear systems. In particular, the proposed observer yields a smaller estimation error norm in comparison with a linear one. It is then shown that cubic observers can be designed to perform similar to linear observers in presence of disturbances and delays. It also compares a cubic observer with a nonlinear extended observer in a simulation example.

The present work addresses the stability issue of switched linear systems (SLSs) subject to event-triggered control. We presume that the feedback controller acquires the switching signals and systems state information only at event-triggered sampling instants. An event triggering strategy is presented for both control and switching. Meanwhile, sufficient condition is obtained to stabilize the event-triggered switched linear systems. In addition, we prove that the Zeno behavior can be excluded through the proposed event triggering strategy. Finally, the speed regulation of a turbofan engine model shows that the derived technique is practicable.

This article is concerned with the event-triggered finite-time H∞ estimator design for a class of discrete-time switched neural networks (SNNs) with mixed time delays and packet dropouts. To further reduce the data transmission, both the measured information of system outputs and switching signal of the SNNs are only allowed to be accessible for the constructed estimator at the certain triggering time instants. Under this consideration, the simultaneous presence of the switching and triggering actions also leads to the asynchronism between the indices of the SNNs and the designed estimator. Unlike the existing event-triggered strategies for the general switched linear systems, the proposed event-triggered mechanism not only allows the occurrence of multiple switches in one triggering interval but also removes the minimum dwell-time constraint on the switched signal. In light of the piecewise Lyapunov-Krasovskii functional theory, sufficient conditions are developed for the estimation error system to be stochastically finite-time bounded with a finite-time specified H∞ performance. Finally, the effectiveness and applicability of the theoretical results are verified by a switched Hopfield neural network.

Consider a network of multiple independent stochastic linear systems where, for each system, a scheduler collocated with the sensors arbitrates data transmissions to a corresponding remote controller through a shared contention-based communication network. While the systems are physically independent, their optimal controller design problems may, in general, become coupled, due to network contention, if the schedulers trigger transmissions based on state-dependent events. In this article we propose a class of probabilistic admissible schedulers for which the optimal controllers, with respect to local standard LQG costs, have the certainty equivalence property and can still be determined decentrally. Then, two subclasses of scheduling policies within this class are introduced; a non-event-based subclass, so called purely stochastic transmission (PST) policy, and an event-based subclass, both with easily adjustable triggering probabilities at every time step. We then prove that, given a PST policy with given triggering probabilities and an associated closed-loop performance with optimal control law, we can find an event-based scheduler from the proposed subclass and with the same tr

This paper provides a new unified framework for second-moment stability of discrete-time linear systems with stochastic dynamics. Relations of notions of second-moment stability are studied for the systems with general stochastic dynamics, and associated Lyapunov inequalities are derived. Any type of stochastic process can be dealt with as a special case in our framework for determining system dynamics, and our results together with assumptions (i.e., restrictions) on the process immediately lead us to stability conditions for the corresponding special stochastic systems. As a demonstration of usefulness of such a framework, three selected applications are also provided.

This article addresses the robust fixed-time stabilization control problem for generic linear systems with both matched and mismatched disturbances. A new observer-based fixed-time control technique is proposed to solve this robust stabilization problem, provided that the system matrix pair (A,B) is controllable. The ultimate boundedness of the closed-loop system in the presence of mismatched disturbances is proven. An upper bound of the convergence time is provided, which is irrelevant to initial conditions. Finally, a simulation example is presented to show the efficiency of the proposed control design method.

The paper presents distributed output feedback synthesis for both leaderless consensus and leader-follower consensus of linear multi-agent systems subject to jointly connected switching networks. Luenberger type distributed observers are proposed based on the neighborhood output estimation error, and distributed output feedback controllers are developed according to the certainty equivalence principle. To show uniform global exponential stability of the switched closed-loop system, a separation principle for a class of switched linear systems with a cascaded structure is established employing weak Lyapunov function and checking weak zero-state detectability of its ‘`diagonal’' switched subsystems. It is shown that the weak zero-state detectability can be guaranteed by a generalized uniform joint-connected condition of switching networks without any dwell-time constraints.

This article studies the flocking problem of multiagent systems (MASs) with a dynamic virtual leader (VL) for the linear systems subject to time-varying uncertainties and external disturbance problems. We assume that the agents follow a leader with a bounded acceleration, which information is sensed only by the informed agents. We assign an adaptive estimator to each uninformed agent to estimate the velocity of the leader. By deriving a lemma and using a standard adaptive law, we design a continuous adaptive controller for solving the problem under the mentioned challenges, which is also fully distributed. The designed flocking control ensures that the velocity of followers asymptotically is converged to that of the VL. Also, the agents' network remains connected, and no collision occurs among agents. Finally, we present a simulation that illustrates the validity of the results.

This article addresses the problem of global stabilization of continuous-time linear systems subject to control constraints using a model-free approach. We propose a gain-scheduled low-gain feedback scheme that prevents saturation from occurring and achieves global stabilization. The framework of parameterized algebraic Riccati equations (AREs) is employed to design the low-gain feedback control laws. An adaptive dynamic programming (ADP) method is presented to find the solution of the parameterized ARE without requiring the knowledge of the system dynamics. In particular, we present an iterative ADP algorithm that searches for an appropriate value of the low-gain parameter and iteratively solves the parameterized ADP Bellman equation. We present both state feedback and output feedback algorithms. The closed-loop stability and the convergence of the algorithm to the nominal solution of the parameterized ARE are shown. The simulation results validate the effectiveness of the proposed scheme.

It is well known that the "fixed spectrum" {i.e., the set of fixed modes} of a multi-channel linear system plays a central role in the stabilization of such a system with decentralized control. A parameterized multi-channel linear system is said to be "structurally complete" if it has no fixed spectrum for almost all parameter values. Necessary and sufficient algebraic conditions are presented for a multi-channel linear system with dependent parameters to be structurally complete. An equivalent graphical condition is also given for a certain type of parameterization.

In this article, we investigate the synchronization problem of continuous-time linear systems with time-varying output couplings. Both bidirectional and unidirectional couplings are studied. In the former one, each pair of connected subsystems can both measure the outputs consisting of the relative information, whereas in the unidirectional case, it is not necessarily the case and a specific scenario when the output matrices have a common matrix factor is considered. The distributed synchronization protocols are designed and the convergence conditions of the resulting system are established making use of switched systems theory based on the detectability assumption and appropriate graph connectivity conditions. Finally, numerical examples are given to verify the derived results.

This paper considers the locally and globally optimal solutions of the global optimisation problem whose objective function is a max‐plus vector‐valued function and constraint function is a real affine function. The formulas about global optimisation in the existing work are further explained and simplified. The necessary and sufficient conditions for the existence and uniqueness of locally optimal solutions of a single objective are established, which are then used to deduce these of globally optimal solutions. Furthermore, the general formulas of locally and globally optimal solutions are presented, respectively. The local optimisation is then applied to solve the load distribution problem of distributed systems with no globally optimal solution, and the optimal allocation scheme is proposed to complete the overall task at the earliest time. The proposed method is constructive, and the obtained results are illustrated by the numerical examples.

Continuous-time linear systems with periodic coefficients of period T are considered in this paper. We study the question of the controllability interval length of periodic systems. It is known that a controllable T-periodic system is controllable over intervals of the length kT where k is the degree of the minimal polynomial of the monodromy matrix. It is proved that this boundary kT is exact and cannot be decreased. It is proved that controllability of a T-periodic system on some interval of the length sT depends on the initial moment, in general.

A quasi‐differential type event‐triggered model predictive control (ET‐MPC) framework for continuous‐time linear systems with additional disturbances is constructed. Different from the existing ET‐MPC, the triggering condition of the proposed method is focused on the differences of the errors between the actual states and the best prediction sequence at two consecutive sampling moments. Its advantage is that the dynamic characteristics of state changes can be better considered, which will achieve a more effective balance between communication load and system control performance. Then, to deal with persistent disturbances, a time‐varying tightening robustness state constraint is utilized in this scheme, instead of conventional state constraints. Finally, the new ET‐MPC algorithm is designed based on the dual‐mode control framework, and its feasibility, the system closed‐loop stability, as well as avoidance of Zeno behaviour are rigorously verified. Simulations and comparisons are provided to demonstrate the algorithm effectiveness.

Different from uniform quantizer and logarithmic quantizer, this paper proposes a novel quantizer for the design problems of linear systems with quantized feedback. This quantizer is based on spherical polar coordinates and has a desired relation between the quantized vector and the corresponding quantization error. Utilizing the proposed quantizer with infinite data rate, the quadratic stabilization problems of linear systems via state feedback under quantization can be converted respectively to the same problems of the corresponding robust control systems with sector bound uncertainties. From a practical point of view, we are more concerned with quantized feedback control with finite data rate, so a quantizer with finite data rate is proposed for the above problems. Different from the quantizer with infinite data rate, a time-varying bounded quantization region needs to be determined to contain the quantized vector as the system evolves. Under this circumstance, the vector needed to be quantized can not be quantized directly, so we give a quantization method to solve this problem.

A cluster tool consists of several single-wafer processing chambers and a wafer handling robot. A wafer has to wait within a chamber after being processed there until it is unloaded by the robot. Such wafer delays may cause wafer quality degradation or variability due to residual gases and heat in the chamber. The tool operation schedule has to maintain identical timing patterns or schedules for each cycle so as to keep wafer delays constant for every wafer. However, at the beginning of the tool operation, the tool is in an empty state and hence we need to make the tool reach such steady schedule by loading wafers into the tool. In this article, we develop a method of scheduling the robot tasks during the start-up period of a cluster tool to reach a target steady schedule quickly as possible. To do this, we model the behaviors of a cluster tool using timed Petri nets and linear system matrices in the max-plus algebra. By analyzing the matrices, we first identify a class of steady schedules which can be reached from the empty tool. We develop the matrices that explain the schedule evolution of the start-up period before reaching the steady period. By examining the matrices, we develop a method of choosing the most desirable one from such class of reachable steady schedules that can be achieved in the minimum time. We also prove that the schedule also minimizes the time duration of the close-down. Finally, we present computational experiments.