Dynamic Event‐Triggered Prescribed Performance Optimal Secure Control for Nonlinear Systems Under DoS Attacks

ABSTRACT This paper focuses on the problem of optimized fuzzy prescribed performance control for nonlinear strict‐feedback systems under denial‐of‐service (DoS) attacks. DoS attacks disrupt communication channels, leading to the output signal and states of the system unavailable. A switched fuzzy observer is adopted to reconstruct unmeasurable states under DoS attacks. Simultaneously, a simpler prescribed performance error transformation is constructed to constrain the tracking error within a prescribed boundary, which can improve the transient and steady‐state performance of the control system. To achieve optimized fuzzy prescribed performance control under DoS attacks, an event‐based secure optimal control strategy is proposed. Specifically, the controller at each backstepping layer is designed as the optimal solution for the corresponding subsystem, such that the entire backstepping control process is optimized. Due to the difficulty in directly solving optimal solutions, a reinforcement learning (RL) algorithm with an actor‐critic architecture is incorporated into the control design process to obtain approximate optimal controllers. In addition, a dynamic event‐triggering mechanism (DETM) is proposed to improve the utilization rate of the communication resource. Through Lyapunov stability analysis, it is proved that the tracking error always evolves within the performance envelope, and the consumed control resources are minimized. Meanwhile, the Zeno behavior can be effectively eliminated. Finally, the effectiveness of the proposed strategy is verified via a simulation example.