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 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.

This paper delves into the constant voltage output regulation problem for delayed inductive power transfer (IPT) systems with linear matrix inequality region constraints. Different from existing works, the proposed approach not only provides a model-free controller design procedure, but also deals with time-varying delays and packet losses. The main contributions are twofold. First, we propose a novel data-based parameterization of delayed systems. Second, data-based sufficient conditions are derived under which the delayed stability and LMI region constraints are guaranteed and then controller is parameterized. Finally, an experimental example is proposed to demonstrate the accuracy of proposed methods and the robustness of the suggested controllers.

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.

In this paper, an attack-defense framework is proposed for the remote H ∞ state estimation of delayed recurrent neural networks (RNNs). Firstly, an important-data-based (IDB) attack strategy is constructed, which can identify the important packets that play essential roles in the estimation and selectively attack them based on their importance degree from the perspective of the attackers. By targeting the important packets, larger attack damages can be achieved. Then, a resilient state estimator that can resist IDB attacks is developed from the defenders' point of view. Notably, some unrealistic assumptions (e.g., the attacker knowing the system structure and full parameters, the defender knowing the attack rate/parameters) are removed, which makes the proposed method easy to implement. At last, simulation results are presented to show the larger destructive effect of the constructed IDB attack and the efficiency of the proposed resilient H ∞ state estimator .

stability of a class of networked control systems (NCSs) under random denial of service (DoS) attacks and design a sampled-data-based state feedback security controller to mitigate the influence of attacks. Different from the existing random attacks, the information about the maximum duration time of DoS attacks can be captured by introducing a predesigned logical processor. Then, based on the periodic sampling technique, the probability of attack occurrence and the resultant number of maximum allowable consecutive packet dropouts can be calculated, which is quite significant to investigating the security problem of NCSs. A DoS-dependent security controller which makes full use of the attack probability information and the number of attack-induced packet dropouts is designed. A novel networked sampled-data system model is first established that enables us to deal with the random DoS attacks phenomena and the time-varying delay induced by attacks under a uniform framework. By structuring a suitable Lyapunov-Krasovskii functional, the relationship between mean square asymptotic stability and attack characteristics is obtained. Finally, the reliability and applicability of the presented control strategy in eliminating the influence of DoS attacks are validated by two practical engineering applications.

This article presents a novel stochastic event-triggered communication scheme (SETS) and a switching-like$H_{\infty }$control method for networked control systems (NCSs) through an open bandwidth-limited network. To overcome the stochastic Denial of Service (DoS) attacks and save the limited network resource, a SETS is first proposed to decrease the number of packets transmitted while considering the malicious DoS attacks. Compared with the existing event-triggered schemes under a precondition that all triggered packets must be successfully transmitted, the proposed SETS has outstanding flexibility since it allows part event-triggered packets to be lost during the communication process. To fully use the dynamic features of communication in an open network, a switching-like control scheme is well designed, which can actively choose the different controller gains based on the current network Quality of Services (QoSs). Then, a networked closed-loop model is constructed, which considered both the effects of SETS and stochastic DoS attacks in a unified framework. Sufficient conditions for the existence of the switching-like controller are presented to ensure the stability of the networked control system while achieving the prescribed performance index. Gain matrices of the desired switching-like controller and the SETS parameters are reached in the light of the solutions to certain matrix inequalities. Compared with the existing time-invariant control strategy in a transmission interval, higher control performance can be expected since both the dynamic network information and the latest available state are well considered in the proposed communication and control framework. Finally, an illustrative example is given to verify the effectiveness of the proposed scheme.

This paper considers model and designing of networked vibration control with time delays for offshore platforms. According to wave theory and ocean engineering, exosystem is designed to describe irregular wave forces. The model of networked vibration control system for offshore platforms with two buffers is presented. The time delays in sensor-controller and controller-actuator channels are stochastic, bounded, and may bigger than one sampling interval. Based on the model, we design networked predictive feedforward and feedback controller. Stability of system can be guaranteed. Simulation results show that with presented controller, displacement and velocity of offshore platform gets smaller and required control force gets less too, compared with networked predictive state feedback optimal controller.

This paper is concerned with networked output feedback H∞control of an offshore structure under earthquakes. With a networked model of the offshore structure, a networked output feedback H∞control strategy is proposed to suppress seismic vibration of the structure. Then, some delay-dependent stability criteria for the offshore structure system are derived. Simulation results show that compared with traditional network-free controllers, the networked output feedback H∞controller can mitigate vibration of the offshore structure and control cost to a smaller level.