Event-Triggered Self-Learning Parallel Tracking Control for Continuous-Time Nonlinear Systems With Actuator Faults

This article explores an event-triggered self-learning parallel tracking control strategy for continuous-time nonlinear systems with actuator faults. To mitigate the impact of actuator faults, an enhanced performance index function is developed based on the unknown upper bound of such faults. Furthermore, an augmented error nonlinear system is constructed by incorporating the tracking error and control input of the original system, so as to implement parallel tracking control via the adaptive dynamic programming (ADP) approach. By designing an adaptive law, the unknown upper bound of faults is estimated online, which adjusts the control parameters in real-time and compensates for the loss of control effectiveness induced by faults. A critic network is employed to approximate the optimal value function within an event-triggered framework, while a dynamic triggering mechanism is adopted to reduce communication overhead. Through the design of appropriate Lyapunov functions, the asymptotic stability of the closed-loop system is rigorously proven, and the weight estimation error is demonstrated to be uniformly ultimately bounded (UUB). Finally, the simulation results are provided to validate the effectiveness of the proposed control strategy.