This paper studies the problem of adaptive event-triggered dynamic output feedback fuzzy control for nonlinear networked control systems (NCSs). Two crucial factors, packet dropouts and random actuator failure, are taken into consideration simultaneously. Takagi-Sugeno (T-S) fuzzy model is introduced to describe considered systems. The Bernoulli random distribution process is employed to depict the phenomenon of data missing. A stochastic function, which is related to Markovian variables, is adopted to depict the actuator failure. An innovative adaptive event-triggered mechanism is introduced to save computational resource. In the framework of Lyapunov stability theory, an adaptive event-triggered fuzzy dynamic output feedback controller is designed to guarantee the stochastic stability and $\mathcal{H}_{\infty}$ performance for considered systems. Finally, simulation results are provided to demonstrate the usefulness of the proposed control strategy.

In this article, a fixed-time observer-based sliding-mode control strategy is proposed for a permanent-magnet synchronous motor. Both the current regulation loop (inner loop) and the speed-tracking loop (outer loop) are controlled by the designed fixed-time convergence sliding-mode controller, improving the control performance and guaranteeing the convergence in a fixed-time manner. To guarantee the robustness of the proposed control algorithm, an observer is constructed to estimate and attenuate the parameter uncertainties and load disturbance in the outer loop. The fixed-time convergence of the inner and outer loop error systems are proved. Moreover, a parameter tuning method of the designed controller is proposed to provide guidance for practical applications. Finally, experiments are conducted by using three different control schemes, that is, PI control, super-twisting sliding-mode control, and the controller designed in this article, the results of which show the effectiveness and advantages of the proposed control scheme.

This paper investigates the problem of the resilient control for cyber-physical systems (CPSs) in the presence of malicious sensor denial-of-service (DoS) attacks, which result in the loss of state information. The concepts of DoS frequency and DoS duration are introduced to describe the DoS attacks. According to the attack situation, that is, whether the attack is successfully implemented or not, the original physical system is rewritten as a switched version. A resilient sliding mode control scheme is designed to guarantee that the physical process is exponentially stable, which is a foundation of the main results. Then, a zero-sum game is employed to establish an effective mixed defense mechanism. Furthermore, a defense-based resilient sliding mode control scheme is proposed and the desired control performance is achieved. Compared with the existing results, the differences mainly lie in two aspects, that is, one where a switched model is obtained, based on which the average dwell-time like approach is utilized to derive the resilient control scheme, and the other where the zero-sum game in employed to make the attacks satisfy the concepts of DoS frequency and DoS duration. Finally, simulation results are given to demonstrate the effectiveness of the proposed resilient control approach.

This paper investigates the zero-sum game based secure control problem for cyber-physical systems (CPS) under the actuator false data injection attacks. The physical process is described as a linear time-invariant discrete-time model. Both the process noise and the measurement noise are addressed in the design process. An optimal Kalman filter is given to estimate the system states. The adversary and the defender are modeled as two players. Under the zero-sum game framework, an optimal infinite-horizon quadratic cost function is defined. Employing the dynamic programming approach, the optimal defending policy and the attack policy are derived. The convergence of the cost function is proved. Moreover, the critical attack probability is derived, beyond which the cost cannot be bounded. Finally, simulation results are provided to validate the proposed secure scheme.

Cable-driven parallel robots (CDPRs) have complex cable dynamics and working environment uncertainties, which bring challenges to the precise control of CDPRs. This article introduces the reinforcement learning to offset the negative effect on the control performance of CDPRs resulting from the uncertainties. The problem of controller design for CDPRs in the framework of deep reinforcement learning is investigated. A learning-based control algorithm is proposed to compensate for uncertainties due to cable elasticity, mechanical friction, etc. A basic control law is given for the nominal model, and a Lyapunov-based deep reinforcement learning control law is designed. Moreover, the stability of the closed-loop tracking system under the reinforcement learning algorithm is proved. Both simulations and experiments validate the effectiveness and advantages of the proposed control algorithm.

This article investigates the secure control problem for cyber-physical systems when the malicious data are injected into the cyber realm, which directly connects to the actuators. Based on moving target defense (MTD) and reinforcement learning, we propose a novel proactive and reactive defense control scheme. First, the system $(A,B)$ is modeled as a switching system consisting of several controllable pairs $(A,\mathcal {B}_{l})$ to facilitate the construction of the MTD control scheme. The controllable pairs $(A,\mathcal {B}_{l})$ can be altered to update system dynamics under certain unpredictable switching probabilities for each subsystem, which can prevent the adversaries from effective attacks. Second, both attack detection and isolation schemes are designed to accurately locate and exclude the compromised actuators from a switching sequence. Third, a reinforcement learning algorithm based on the zero-sum game theory is proposed to design the defense control scheme when there exist no controllable subsystems to switch. To demonstrate the effectiveness of the defense control scheme, a three-tank system under unknown cyber attacks is illustrated.