Predefined-Time Event-Triggered Tracking Control for Nonlinear Servo Systems: A Fuzzy Weight-Based Reinforcement Learning Scheme
Hao Shen, Wei Zhao, Jinde Cao, Ju H. Park, Jing Wang
Abstract
In this paper, a novel reinforcement learning-based predefined-time control method for nonlinear servo systems with prescribed performance is proposed under an event-triggered strategy. Firstly, the nonlinear dynamics and control behaviors of the systems can be trained effectively through fuzzy logic systems under the identifier-critic-actor framework. Moreover, by employing the prescribed performance control and a switching event-triggered rule, system tracking performance can be ensured while decreasing the data transmission frequency. With the assistance of the predefined-time stability criteria, the boundedness of system variables and the convergence of tracking errors within a predetermined time can be guaranteed. Comparisons with some existing control schemes are addressed regarding tracking performance and action costs. The availability and superiority of the suggested scheme are verified in the simulations.