Event-triggered Optimal Adaptive Control for Robot Trajectory Tracking

Abstract

In this paper, an event-triggered optimal adaptive control is developed for robot trajectory tracking system. Due to the nonlinearity of Hamilton function, we apply the actor-critic neural network structure to solve it. Firstly, the critic network is used to estimate the cost function and the actor network is used to estimate the optimal event-triggered control law. Due to the advantage of event-triggered method, the weight update rate of actor-critic neural network only occurs when the triggering condition is violated, which save a lot of communication resources. Then, the event-triggered robot trajectory tracking system is ultimately bounded by Lyapunov stability analysis. Finally, the simulation show that the proposed method is effective.