Adaptive Neural Event-Triggered Fault-Tolerant Control for Uncertain Nonlinear Cyber-Physical Systems With Sensor and Actuator Faults Via Triggered Output Feedback
针对不确定非线性信息物理系统，同时考虑未知动态、时变传感器和执行器故障，提出一种仅利用触发故障输出的自适应神经事件触发输出反馈容错控制方案，保证闭环信号有界且系统输出渐近收敛到零。
This article is concerned with the event-triggered fault-tolerant control (FTC) for uncertain nonlinear cyber-physical systems (CPSs) by only exploiting the triggered faulty output. During the control design process, the unknown system dynamics, the time-varying sensor, and the actuator faults are considered simultaneously. Based on the event-triggered mechanism, the first-order filter technique and the nonlinear impulsive dynamics approach, an adaptive neural event-triggered output feedback FTC scheme is established. More specifically, one triggering condition is established for both the measurable output and the state estimations, with the adaptive parameters being triggered at the same instants. Another triggering condition is established for the controller, eliminating the need for real-time monitoring of control information and thereby reducing the computational burden. Then, a neural state observer is designed from triggered faulty output and triggered state estimations. The first-order filter technique is introduced to handle the non-differentiability of virtual controls stemmed from the event-triggered mechanism. The nonlinear impulsive dynamics approach is employed for stability analysis of the discontinuous error dynamics. It is proved that, with the proposed scheme, all the closed-loop signals are bounded, meanwhile the system output converges to the origin asymptotically, and the Zeno behavior is excluded. Finally, simulation results present the feasibility and effectiveness of the seeking schemes.