This article addresses the lateral dynamics control problem for autonomous vehicle systems under randomly perturbed sampling (RPS) periods and the FlexRay communication protocol. To capture vehicle nonlinearities under variable-velocity conditions, a T-S fuzzy model is constructed using longitudinal velocity as the premise variable. The random sampling behavior caused by hardware aging and environmental disturbances is modeled as a Markovian process. Then, measured outputs are transmitted under the FlexRay protocol (FRP) that integrates both time-driven (static) and event-driven (dynamic) scheduling characteristics. By fully analyzing the situation of static and dynamic scheduling, a unified compensation strategy is employed to build a new switching output model reflecting the impact of the FRP on the measured outputs. Based on this output model, a sampling-mode-dependent fuzzy controller is designed to handle random sampling and hybrid scheduling issues, which results in a membership asynchronous phenomenon between the autonomous vehicle model and controller. By using the asynchronous constraint technique, sufficient conditions with low conservatism are derived to guarantee stochastic stability and $H_{\infty }$ performance of the closed-loop system. Furthermore, a comprehensive optimization problem (OP) is established, and a corresponding genetic algorithm (GA) is presented to provide a solution-solving scheme. Simulation results confirm the effectiveness and superiority of the proposed control strategy under complex communication environments.

This article investigates the sliding mode control (SMC) problem for a class of Markov jump systems (MJSs), in which the system states are sampled randomly and nonuniformly according to Markov chain. Besides, the transmission of sampled states through the shared network channel is inevitably subject to deception attacks obeying Markov model. In order to facilitate the subsequent design and analysis, the encountered three Markov chains are first mapped into one, meanwhile, a suitable mode detection scheme is put forward to simultaneously detect the partially inaccessible modes including the controlled system modes and attack modes. And then, a detected-mode-dependent sliding mode controller is designed to effectively cope with the stochastic features of sampling processes and attack occurrences. Furthermore, the reachability of the specified sliding surface and the mean-square exponential ultimate boundedness of the closed-loop system are analyzed and the corresponding conditions are derived. Finally, two simulation examples are provided to illustrate the designed control method.

This article investigates the security challenges associated with integrated attack-defense strategies for multiarea power systems (MAPSs). Traditional attack models often employ indiscriminate approaches that overlook the heterogeneous significance of different area subsystems. In practice, however, these areas contribute unequally to the overall stability and security of MAPSs. To address this gap, an area-significance-driven attack model is introduced, in which the importance of each area is quantitatively evaluated based on its contribution to global system stability and operational security. This targeted approach enables adversaries to maximize system disruption by selectively exploiting high-impact components. In parallel, an adaptive defensive control strategy is proposed to dynamically respond to the characteristics of detected attacks, thereby mitigating damage and enhancing system resilience. By coupling targeted attack modeling with adaptive defensive control, a unified framework is established that characterizes the interactive dynamics between adversarial threats and system responses in MAPSs. Finally, simulations on a three-area power system confirm the disruptive potential of area-significance-driven attacks and demonstrate the effectiveness of the proposed defense strategy in maintaining system-wide stability under adversarial conditions.

A security control problem is addressed for a kind of networked Markov jump system subject to two-side stochastic denial-of-service (DoS) attacks, in which the attack occurrence situation obeys the Markov chain. To resist the attack effects, a redundant channel protocol is adopted, wherein measurement/control signals are sent to the controller/actuator via the redundant channel when the primary channel suffers from cyber-attacks. According to the above redundant channel protocol, both measurement and input models are established to represent the received signals by the controller and actuator, respectively. Then, a novel two-layer mapping strategy via logical operations is proposed to describe attack occurrences and system jumping. These facilitate the design of a security sliding mode controller, under which the exponential mean-square stability of the networked Markov jump systems subject to DoS attacks and the corresponding sufficient conditions are derived. Eventually, the simulation results via the DC chopper circuit system are provided to illustrate the proposed redundant-channel-based security control method.Note to Practitioners—This paper is motivated by the attack resistance problem in networked control systems. With the revolutionary evolution of wireless communication technology, in many practical systems, such as smart power grids, unmanned aerial vehicles (UAVs), and unmanned surface vehicles (USVs), the physical devices including sensors, actuators, controllers, and other intelligent devices are interconnected and transmitted the data through a network infrastructure. The inevitable DoS attacks may cause reliability degradation of data transmission, which in practice results in shaky grid frequency, failed UAV formation, and inaccurate USV detection. Distinguished from the previous investigation results from a passive perspective, this paper proposes a redundant-channel-based method to solve the reliability degradation problem caused by DoS attacks from a proactive perspective. The developed redundant channel method against Markov-chain-based DoS attacks can improve the reliability of data transmission, and the proposed novel two-layer mapping strategy via logical operations can increase the freedom of security controller design. A DC chopper circuit is used to verify the effectiveness of the proposed novel control method and comparison simulation results show the advantages of the redundant channel method. How to improve the reliability of vulnerable channels is a knotty problem in communication. With the groundbreaking research of this paper, against other attacks, such as deception attacks, reply attacks, and frequency-and duration-constrained DoS attacks, the attack resistance problems via proactively strengthening the reliability of data transmission should be further explored in the future.

This article addresses the containment control problem in multi-unmanned aerial vehicles (multi-UAVs) over communication networks subject to Markov switching topologies and channel fading. Firstly, a decoupling method is skillfully proposed to divide the closed-loop global containment error system into multiple local dynamics, simplifying the design of the distributed self-triggered scheme. Then, a mode-dependent distributed containment controller is designed, leveraging fading depths to mitigate the impact of channel fading. During the containment analysis, a self-triggered auxiliary condition is intelligently derived, forming the basis for the subsequent design of the self-triggered mechanism. Additionally, a novel distributed self-triggered mechanism is designed to significantly reduce the frequency of information exchange in the system while eliminating Zeno behavior. Finally, the effectiveness of the proposed distributed self-triggered containment control is validated through a simulation example.Note to Practitioners—In a multi-UAV system, stable operation primarily depends on information transmission between UAVs. Network-induced constraints will severely undermine system stability and jeopardize coordinated control. Accordingly, studying effective solutions to address network-induced constraints in real-world systems is of practical importance. This paper investigates the containment control problem in a multi-UAV system affected by channel fading, random communication topologies, and self-triggered transmission. By incorporating fading depth in the communication channel, the proposed controller adapts to varying channel conditions, ensuring the UAVs maintain coordination even under poor communication conditions. Additionally, given that the communication topology between UAVs may change due to environmental factors in real-world applications, Markov random switching topologies can be used to model this phenomenon. Limited communication resources may lead to transmission delay or even network congestion due to the large amount of information exchanged between UAVs. Nevertheless, blindly increasing network bandwidth not only imposes high demands on sensors but also raises experimental costs. Therefore, reducing the frequency of information transmission is a commonly used approach. Unlike traditional event-triggered scheme, this paper proposes a distributed self-triggered scheme for addressing containment control problems in multi-UAV systems, eliminating the need for continuous event monitoring. From a practical application perspective, the distributed self-triggered approach does not require additional sensors for event monitoring, further reducing costs. Building on this work, future research should also address the formation control of multi-UAV systems, where multiple UAVs cooperate to form a specific formation through information exchange.

This article proposes a novel nonsingleton try-once-discard (TOD) scheduling scheme for networked control systems (NCSs) under the semantic-driven communication, aimed at avoiding node collisions while simultaneously enhancing system operational efficiency. First, a research framework is established for NCSs operating within the semantic-driven communication paradigm. Second, a method for semantic extraction based on natural language is introduced. Subsequently, the "cultural clash" between semantic interactions and bitstream-based system components is addressed through the application of fuzzy mathematical methods. Under such semantic-driven communication mechanism, raw data is successfully compressed. Building upon the efficient compression of data, a nonsingleton TOD scheduling scheme is presented. In comparison to the traditional TOD scheduling approaches that activate only one node per transmission, the proposed scheduling scheme facilitates the simultaneous transmission of information from multiple nodes, thereby enhancing system efficiency greatly. By data-modeling semantic disparities and designing the appropriate controller, the input-to-state stability of the studied system is guaranteed, even when confronted with certain levels of semantic discrepancies. Moreover, an algorithm is provided to optimize semantically-related parameters, so as to reduce semantic discrepancies as much as possible. Finally, the effectiveness of the proposed method is verified by a six-area power system.

This paper is concerned with the security issues related to integrated attack-defense strategy for a category of multi-sensor networked control systems with state saturation constraints. In general, existing denial-of-service (DoS) attack models typically conduct indiscriminate attacks on data packets, disregarding the significance of the attacked data packets to the system. Note that the measurement data from different sensor nodes possesses varying levels of importance. In light of this, we first propose a novel form of attack from the perspective of attack design, known as a data-importance-aware attack. The importance of data refers to the quantitative impact of the measured values at each sensor node on the stable and safe operation of the entire system. As such, the proposed attack has the awareness to launch attacks against critical sensor nodes, rendering data unable to be transmitted. Then, an attack-node-dependent security controller is devised from the defender’s perspective against the constructed attack, which can effectively resist the impact of attacks and stabilize the system. By employing the Lyapunov functional method, sufficient conditions are derived to ensure the asymptotic stability of the closed-loop system. Finally, the reliability and effectiveness of the node importance-aware attack strategy and control countermeasure are validated by numerical simulation.

This article investigates a semantic-aware multipacket parallel transmission (MPPT) scheme for networked control systems (NCSs) using an autoencoder-based encoding–decoding framework. First, semantic encoding and decoding schemes are proposed based on autoencoder technology, aiming to effectively enhance the information density of data packets and reduce bandwidth usage during MPPT in NCSs. Through abstract semantic representations, raw data are mapped into a lower dimensional space for semantic encoding. Subsequently, these semantic representations are transmitted and utilized for semantic decoding, achieving the reconstruction of raw data. Considering multiple factors, such as semantic decoding errors, delays, and disturbances, stability analysis and$H_\infty$controller design methods of NCSs under the semantic-aware MPPT scheme are explored based on Lyapunov stability theory. Using gradient descent optimization, semantic encoding–decoding parameters are pretrained offline to enhance the collaborative efficacy of semantic encoder and decoder, concurrently meeting real-time requirements of NCSs. Guided by operational feedback, subsequent parameter refinement is achieved through an incremental search algorithm, aiming to improve the performance of data reconstruction. Finally, the effectiveness of the proposed method is demonstrated through the example of a six-area power system.

Dynamics in supply chains (SCs) can trigger risks due to the changing and propagating nature. In the context of COVID-19, this article presents an assessment-to-control decision-making support scheme to tackle propagation effect uncertainties of SCs considering product changes. First, a new decision model is proposed for risk warnings, with the potential advantages that: 1) propagation effects can be assessed generally and objectively and 2) permitting control theory to integrate and identify the interrelations between propagation effects. More specifically, the bullwhip effect (BE) with operational and behavioral causes is quantified as cascading amplified inventory fluctuations. The ripple effect (RE) from large-scale supplier disruptions driven by COVID-19 is quantified as increased entropy rates (ERs). Then, the system studied is integrated as a closed-loop control system under provided change control. Moreover, some criteria are derived for the existence of controller gains/decision coefficients to stabilize the closed-loop system with the BE mitigation under the RE. Finally, a mask SC case study under COVID-19 is performed for examining the effectiveness of the proposed scheme.

In this article, the sliding mode control (SMC) problem is addressed for two-dimensional (2-D) systems depicted by the second Fornasini-Marchesini (FMII) model. The communication from the controller to actuators is scheduled via a stochastic protocol modeled as Markov chain, by which only one controller node is permitted to transmit its data at each instant. A compensator for other unavailable controller nodes is introduced by means of previous transmitted signals at two most adjacent points. To characterize the features of 2-D FMII systems state recursion and stochastic scheduling protocol, a sliding function associated with the states at both the present and previous positions is constructed, and a scheduling signal-dependent SMC law is designed. By constructing token- and parameter-dependent Lyapunov functionals, both the reachability of the specified sliding surface and the uniform ultimate boundedness in the mean-square sense of the closed-loop system are analyzed and the corresponding sufficient conditions are derived. Furthermore, an optimization problem is formulated to minimize the convergent bound via searching desirable sliding matrices, meanwhile, a feasible solving procedure is provided by using the differential evolution algorithm. Finally, the proposed control scheme is further demonstrated via simulation results.

This article is concerned with the sliding mode control (SMC) problem for a class of Markov jump systems subject to packet dropouts, in which the dropped or received status of packet is described by a Markov chain. To enhance the reliability of data transmission, multiple redundant channels are employed between sensors and the controller. Different from the existing measurement model under redundant channels with Bernoulli-process-based packet dropouts, a novel measurement model under Markov-chain-based packet dropouts is proposed under the redundant channel transmission. It is assumed that the modes of the controlled system and packet-dropout model are unavailable, and then a mode detection mechanism is proposed to detect the partially unavailable modes. By utilizing the detected modes, a dynamic observer is constructed to estimate the unmeasurable system state, based on which a detected-mode-dependent sliding mode controller is designed to achieve the mean-square exponential ultimate boundedness of the closed-loop system. Meanwhile, incremental search technique and particle swarm optimization algorithm are, respectively, utilized to solve two optimization problems for enhancing the closed-loop performances. Finally, two simulation examples are provided to verify the effectiveness of the proposed schemes.

In this article, the sliding mode control problem is addressed for a class of sampled-data systems subject to deception attacks. The sampling periods undergo component-wise random perturbations that are governed by a Markovian chain. The component of the sampled output is transmitted via an individual communication channel that is vulnerable to deception attacks, and Bernoulli-distributed stochastic variables are utilized to characterize the random occurrence of the deception attacks initiated by the adversaries. A sliding mode controller is designed to drive the state into the sliding domain around the specified sliding surface, and sufficient conditions are derived to guarantee the exponentially ultimate boundedness of the resultant closed-loop system in the mean-square sense. Furthermore, an optimization problem is established to pursue locally optimal control performance. Finally, a simulation example is given to verify the effectiveness and advantages of the developed controller design approach.

This article addresses the problem of dynamic event-triggered (DET) control for networked interval type-2 (IT2) fuzzy systems subject to multiple cyber-attacks, which contain deception attacks and aperiodic denial-of-service attacks simultaneously. First, a multiple cyber-attack model is established for the IT2 fuzzy systems with multiple cyber-attacks and external disturbances. Second, an improved DET mechanism (DETM) is proposed to reduce the communication burden. Contrasting with existing works, by introducing two adjustable parameters into the novel DETM, the presented DETM has more flexibility in adjusting the data packet transmissions to conserve more limited network resources. Meanwhile, the Zeno behavior is probably excluded under the proposed DETM while preserving the system performance. Third, imperfectly matched membership functions (MFs) are considered between fuzzy controller and IT2 fuzzy model, and the global boundary information of MFs and slack matrices are introduced to relax stability conditions. Besides, by constructing a proper Lyapunov–Krasovskii functional, sufficient conditions are obtained to ensure the exponentially mean-square stable of the IT2 fuzzy systems with the$H_{\infty }$performance. Finally, two simulation examples are provided to illustrate the effectiveness of the theoretical results.

In this article, the sliding mode control (SMC) problem is addressed for a class of Markovian jump systems via the T–S fuzzy model. First, in order to reduce the frequency of state transmission for alleviating congestion phenomenon in the bandwidth-limited communication network, a dynamic event-triggered (DET) strategy is introduced into the sensor-to-controller channel, in which an additional internal dynamical variable is employed to adjust the event-triggered condition adaptively. A fundamental issue resulting from the event-triggered strategy is that the controller cannot obtain the information about system mode during the triggering interval. Aiming at the phenomenon, this work utilizes a mode detector to estimate the unavailable system mode. Then, this article proposes a detected-mode-dependent event-triggered sliding mode controller whose membership grades are determined only via the transmitted state at the triggering instant. By constructing a relation on the membership functions (MFs) between the fuzzy model and the controllers for MF-dependent analysis, the conditions on the reachability and stability conditions are relaxed. Furthermore, an optimization algorithm is provided for the minimum control power via a high-dimensional grid searching for the coefficients of the internal dynamic variables, which, together with the designed detected-mode-dependent sliding mode controller, constitutes the novel SMC scheme under the DET strategy. Finally, the simulation results via the single-link arm system are provided to illustrate the efficiency of the proposed method.

This article addresses the security control problem of a class of interval type-2 fuzzy systems via the sliding mode control strategy. A stochastic communication scheduling protocol is utilized to govern the transmission from the sensors to the controller, by which only one sensor node has the chance to transmit its value at every instant. Meanwhile, cyber attacks from malicious adversaries might be launched in vulnerable communication channels. To quantitatively analyze the effect of the stochastic communication protocol and cyber attacks, their mathematical model is first constructed based on a compensation scheme. Since the scheduling signal may be unavailable once cyber attacks are activated, a desirable sliding mode control law is synthesized with token-independent control gains, whose membership functions are mismatched with those of the fuzzy system. To deal with these mismatched membership functions, the relations between the membership functions of the system and the control law are reconstructed. Consequently, the favorable property of perfectly matched memberships could be employed. Sufficient conditions are derived so that the resultant closed-loop interval type-2 fuzzy system is stochastically stable and, at the same time, the state trajectories can be forced into a small domain around the prescribed sliding surface. The proposed control design approach is verified by two examples.

This article investigates the hybrid sliding mode control problem for the uncertain Markovian jump systems (MJSs) via the transition rates optimal design. The stability condition for the transition rates is first established to ensure the exponential mean-square (EMS) stability of the unforced uncertain MJSs. Then the hybrid design strategy on the sliding mode controller and transition rate matrix is presented to ensure the EMS stability of the controlled system. Moreover, the iterative optimization algorithms are developed to acquire the desirable transition rates, control gain, and decay rate$\sigma $. Finally, some numerical simulation results are provided.

The adaptive sliding mode control (SMC) problem is addressed for singular semi-Markovian jump systems (S-MJSs) against actuator attacks, in which the transition rates rely on the random sojourn time and are not constant, and the system states are unavailable. Moreover, the vulnerability of control signals transmitted via communication network means that the actuators may receive the attacked control signals. For the sake of reducing the effect of actuator attacks, the neural network technique is used to approximate the false information injected by adversaries. Meanwhile, a sliding mode observer is introduced to estimate the unmeasured states. An adaptive SMC law is proposed to guarantee that the estimation states and errors can reach to the sliding surfaces, and the stochastic admissibility of the singular S-MJSs can be ensured. In the end, an example is applied to illustrate the method in this paper.