Event-Triggered Optimal Tracking Control for Uncertain Nonlinear System Based on Reinforcement Learning

In this paper, an event-triggered optimal tracking control problem is studied for uncertain nonlinear system based on reinforcement learning (RL). Firstly, a class of nonlinear dynamic system with general uncertainty is considered and the augmented system comprising tracking error and reference signal is constructed. Secondly, an improved adaptive dynamic programming (ADP) technique, involving actor-critic algorithm and fuzzy logic systems, is developed to solve the Hamilton-Jacobi-Bellman (HJB) equation with respect to nominal augmented system. Thirdly, in order to reduce the mechanical wear of actuator and energy consumption, event-triggered mechanism is performed in controller updating. Finally, stability analysis proofs that all signals are uniformly ultimately bounded (UUB) in the closed-loop system via Lyapunov theory. Simulation results verify feasibility of proposed scheme.