This study addresses the maneuvering control problem of a wheeled mobile robot (WMR) operating in dynamic and uncertain environments, where smooth error convergence and robustness against external disturbances are critical challenges. An enhanced Lyapunov-based model predictive control (ELMPC) method is developed, in which an improved tunnel performance function is incorporated into an auxiliary controller to impose stricter contraction constraints on the WMR system, enabling smoother and faster error convergence. In addition, an improved tunnel performance constraint (ITPC) scheme is introduced to eliminate the initial-condition dependence of conventional prescribed performance control. By employing a performance transition function, the ITPC ensures that performance bounds are satisfied under arbitrary initial errors while avoiding excessive transient responses. To compensate for unknown and time-varying disturbances, a neural network–based disturbance predictor is integrated into the control architecture. The predictor estimates disturbances online and transforms tracking errors into prediction errors, thereby accelerating convergence and enhancing robustness. The proposed control strategy is implemented within a Lyapunov-based model predictive control framework with explicit consideration of actuator constraints, where contraction constraints guarantee closed-loop stability and recursive feasibility. Experimental results demonstrate improved tracking performance, robustness, and constraint satisfaction under complex operating conditions.

This article presents a sliding mode control (SMC) strategy to address the finite-time consensus problem of multiagent systems (MASs) under denial-of-service (DoS) attacks. Agents exchange information over network channels that are vulnerable to stochastic DoS attacks, which may disrupt communication and change the network topology. To capture these stochastic variations, a Markov jump model is employed to describe the switching of communication topologies. By introducing a disagreement vector, the consensus problem of the MAS within a finite-time interval is transformed into the stochastic finite-time boundedness (SFTB) problem of the disagreement error dynamic system. A feasible SMC law is developed to drive the disagreement error dynamic system onto a specified sliding surface within a finite time. Furthermore, a partitioning policy is used to ensure the SFTB of the system during both the reaching phase and the sliding phase. A reduced-order approach is used to resolve potential uncontrollability in the system, and sufficient conditions are established to ensure the SFTB of the disagreement error dynamic system under the proposed SMC strategy. Finally, a multiaircraft system example is provided to demonstrate the correctness and effectiveness of the proposed approach.

This paper aims to address the design problem of asynchronous controllers for networked jumping systems (NJSs), in which two communication channels are vulnerable to asynchronous denial-of-service (DoS) attacks. To this end, a hidden Markov model (HMM)-based active security control strategy is proposed to counter asynchronous dual-channel DoS attacks. Firstly, two maximum consecutive numbers are introduced to describe DoS attacks randomly initiated by adversaries. Subsequently, a mode-dependent predictor is designed to generate predictive states, which can be employed in the switched controller to stabilize the NJSs. Furthermore, sufficient conditions are derived by constructing Lyapunov functions to ensure the asymptotic mean-square stability of the NJSs under asynchronous dual-channel DoS attacks. Finally, a simulation using a space robot manipulator model is presented to validate the effectiveness and practicality of the proposed active security control strategy.

This work addresses the design problem of the fault detection observer (FDO) based on dynamic event-triggered mechanism for Markov jump systems under denial-of-service (DoS) attacks. The concept of limited energy for attackers is employed to characterize the property of nonperiodic DoS attacks. A dynamic event-triggered mechanism is introduced to save the system's communication resources. The $H_{\infty }/H_{-}$ index is incorporated to ensure that the designed FDO possesses both robustness against disturbances and sensitivity to faults. After obtaining a set of nonlinear inequalities using Lyapunov functional techniques, a simulated annealing algorithm is employed to assist in solving, ensuring not only the discovery of global optimization solutions but also obtaining satisfactory parameters for the dynamic event-triggered mechanism. Finally, the effectiveness of the designed FDO is illustrated by an example of a vertical take-off and landing vehicle dynamical system.

This article concentrates on the sliding mode control (SMC) problem of nonlinear unmanned marine vehicles (UMVs) over a finite time horizon. In view of the nonlinearity and variability of the marine environment, the UMVs are characterized by the Takagi-Sugeno (T-S) fuzzy system. To improve the utilization of network resources, a novel adaptive event-triggered protocol is proposed. This protocol can dynamically adjust the threshold parameters in response to denial-of-service (DoS) attacks. An integral sliding mode controller is designed, which can achieve the ideal sliding mode within any given brief time interval. Through the Lyapunov theory, sufficient conditions for finite-time$\mathcal {H}_{\infty }$control are given. Compared with existing results, our method presents more robust performance and faster convergence rates. Finally, simulations are presented to confirm the feasibility and effectiveness of the proposed method.

This article concerns the stochastic generalized Nash equilibrium problem (NEP) characterized by uncertain expected value cost functions and shared constraints. In a full-decision information setting, we develop a novel distributed stochastic reflected forward–backward (FB) algorithm, which requires that each agent has access to the others’ decisions. Considering that agents only know the decisions from their immediate neighbors, a distributed stochastic RFB (SRFB) algorithm under partial-decision information is proposed. By recasting the problem as a monotone inclusion problem, both algorithms almost surely converge to a stochastic generalized Nash equilibrium by combining the stochastic approximation scheme and the variance reduction scheme. Finally, the numerical experiment validates the feasibility of the proposed algorithms and confirms the correctness of the theory.

In this work, we address the issue of asynchronous deconvolution filter design for 2-D Markov jump systems with random packet losses. First, the considered plant is established by a well-known Fornasini-Marchesini model. Then, an asynchronous 2-D deconvolution filter is proposed to reconstruct the 2-D signal with measurement noise to satisfy a prescribed performance specification. The asynchronization phenomenon between the system modes and filter modes is characterized by a hidden Markov model. Besides, in practical applications, the congestion of the transmission channel between the system and the filter may lead to data losses, which may make the system performance degraded or even unstable. For this, an improved 2-D single exponential smoothing scheme is proposed to generate some predictions of the lost information to compensate for lost packets. By means of the 2-D Lyapunov stability theory, some sufficient conditions are acquired, which can make the resultant system asymptotic mean-square stable and satisfies an$\mathcal{H}_{\infty}$disturbance attenuation performance. At last, an example concerning image processing is adopted to verify the correctness of the presented asynchronous 2-D deconvolution filtering scheme.Note to Practitioners—In practical applications, many dynamics may suffer from undergoing sudden structural or parameter changes, resulting in a system that is difficult to describe clearly. The Markov jump systems, consisting of states and modes, can handle this problem satisfactorily. Considering the effects of some unfavorable factors, i.e., delay, quantization, and environmental noise, a hidden Markov model is employed to handle the asynchronous problem between the system and the filter. On the other hand, the emergence of 2-D systems effectively solves the problem of the system’s state evolving in two directions. In addition, the congestion of the transmission channel between the system and the filter may lead to data loss. To compensate for the impact of data packet loss, an improved 2-D single exponential smoothing scheme is proposed.

This article presents a sliding mode controller to address the challenge of achieving mean-square bounded consensus tracking for leader–follower multiagent systems (MASs) under stochastic denial-of-service (DoS) attacks. Such cyber attacks can reduce the effective transmission of measurement signals by interrupting the communication between the MASs and the control station, thereby corrupting the feasibility of control. Existing descriptions of DoS attacks typically rely on two energy assumptions regarding attack frequency and duration, which introduce conservatism into the stability analysis of the system. Conversely, this article models DoS attacks using a two-mode Markov process, thereby preventing the necessity for explicit energy constraints. To ensure control feasibility under DoS attacks, a control scheme that uses the latest uncontaminated control input signal and uses it as the new primary control input signal until the DoS attack ceases is adopted to mitigate the effects of DoS attacks effectively. Based on the Lyapunov function method, it is shown that the designed sliding mode controller guarantees the reachability and mean-square bounded consensus tracking of the resulting global tracking error dynamic system under Markov-type DoS attacks. At last, the correctness and the effectiveness are verified by a web-based multiple dc motors angle coordinated control experiment.

This work solves the issue of event-triggered fault detection for networked systems under the denial-of-service (DoS) attacks. To improve the utilization rate of network resources, an event-triggered mechanism is employed to reduce the transmission frequency. A fault detection observer is designed to generate the residual signal for the concerned system with external disturbances and faults. Note that the input signal of the fault detection observer (FDO) transmitted over a communication network is assumed to be vulnerable to cyber attacks, in which the adversaries may interrupt the transmission process. The${\mathcal {H}}_\infty$attenuation index and${\mathcal {H}}_{\_}$increscent index are introduced into the fault detection observer design, which reflects the robustness to external disturbances and sensitivity to faults, respectively. By applying the Lyapunov functional technology, some nonlinear matrix inequalities are acquired to guarantee the existence of the fault detection observer with the appearance of DoS attacks. Then, a genetic algorithm is adopted to cope with the derived nonlinear matrix inequalities without introducing any conservatism. The simulation results related to an unmanned aerial vehicle model are presented to illustrate the correctness and effectiveness of the presented fault detection strategy.

This article concerns the co-design scheme of the adaptive event-triggered mechanism (AETM) and asynchronous$H_{\infty }$control for two-dimensional (2-D) Markov jump systems. First, we introduce a hidden Markov model with the observation that the asynchronous phenomenon is inevitable between the plant mode and the controller mode. Besides, for economizing the communication times, an innovative 2-D AETM is constructed, which can dynamically regulate the event-triggered thresholds to strive for better system performance. Then, by utilizing the 2-D Lyapunov stability theory, nonlinear matrix inequalities are built to ensure the asymptotic mean-square stability with an$H_{\infty }$performance for the closed-loop 2-D system. To avoid introducing any conservatism when handling the above nonlinear matrix inequalities, a binary-based genetic algorithm (BGA) is exploited to treat some variables as known, such that derive some directly solvable linear matrix inequalities. Finally, a simulation example is provided to verify the effectiveness of the proposed 2-D AETM-based asynchronous controller strategy with a BGA.

In this work, we consider the problem of finite-region asynchronous dissipative control and pay more attention to the transient behavior of a class of two-dimensional fuzzy Markov jump systems (MJSs). First, the considered plant is modeled based on a well-known Fornasini–Marchesini equation. The asynchronization phenomenon between the system modes and controller modes is characterized by a hidden Markov model. Then, by a fuzzy-basis-dependent and mode-dependent Lyapunov function, sufficient conditions are established, which can make the overall closed-loop fuzzy dynamic MJSs be finite-region bounded with a strictly$(T, S, R)$-$\theta $-dissipative performance. Finally, a numerical example concerning the Darboux equation is employed to validate the effectiveness and performance of the presented control scheme.

This article addresses the design issue of fuzzy asynchronous fault detection filter (FAFDF) for a class of nonlinear Markov jump systems by an event-triggered (ET) scheme. The ET scheme can be applied to cut down the transmission times from the system to FAFDF. It is assumed that the system modes cannot be obtained synchronously by the filter, and instead, there is a detector that can measure the estimated modes of the system. The asynchronous phenomenon between the system and the filter is characterized via a hidden Markov model with partly accessible mode detection probabilities. Applying the Lyapunov function methods, sufficient conditions for the presence of FAFDF are obtained. Finally, an application of a wheeled mobile manipulator with hybrid joints is employed to clarify that the devised FAFDF can detect the faults without any incorrect alarm.

In this article, the problem of the asynchronous fault detection (FD) observer design is discussed for 2-D Markov jump systems (MJSs) expressed by a Roesser model. In general, the FD observer cannot work synchronously with the system, that is, the mode of the observer varies with the mode of the system in line with some conditional transitional probabilities. For dealing with this difficult point, a hidden Markov model (HMM) is employed. Then, combining the$H_{\infty }$attenuation index and$H_{\_{}}$increscent index, a multiobjective solution to the FD problem is formed. In terms of linear matrix inequality technology, sufficient conditions are gained to guarantee the existence of the asynchronous FD. Simultaneously, an asynchronous FD algorithm is generated to acquire the optimal performance indices. Finally, a numerical example concerned with the Darboux equation is demonstrated to exhibit the soundness of the developed approach.

Based on the interval type-2 fuzzy (IT2F) approach, this article investigates the fault detection filter design problem for a class of nonhomogeneous higher level Markov jump systems with uncertain transition probabilities. Considering that the mode information of the system cannot be obtained synchronously by the filter, the hidden Markov model can be seen as a detector to handle this asynchronous problem, and the parameter uncertainty can be processed by the IT2F approach with the lower and upper membership functions. Then, the asynchronous IT2F filter is designed to deal with the fault detection problem. Furthermore, the Gaussian transition probability density function is introduced to describe the uncertainty transition probabilities of the system and the filter. Based on the Lyapunov theory, the existence of the designed asynchronous IT2F filter and the dissipativity of the filter error system can be well ensured. In this article, the simulation study on a quarter-car suspension system verifies that the designed asynchronous IT2F filter can detect faults without error alarms.

This article focuses on the finite-time asynchronous output feedback control scheme for a class of Markov jump systems subject to external disturbances and nonlinearities. The conic-type nonlinearities hold a constraint condition which locates in a known hyper-sphere with an indefinite center. In addition, the asynchronization phenomenon occurs between the system and the controller, which can be represented by means of a hidden Markov model. A sufficient condition is derived not only to guarantee the finite-time boundedness of the acquired closed-loop systems but also to possess a desired$H_{\infty }$performance on the basis of Lyapunov functional technique. Finally, the validity and feasibility of the proposed method are demonstrated with a dc-motor experiment.