Event-Triggered Robust Constrained Control of Uncertain Nonlinear Systems With Input Saturation Based on Self-Learning Disturbance Observer
Shuyi Shao, Xiaohui Yan, Mou Chen, Z. G. An
Abstract
In this paper, an adaptive event-triggered constrained control strategy is proposed for uncertain nonlinear systems with input constraints by using reinforcement learning technology and disturbance observer. By constructing an Actor-Critic neural network (NN) framework, the unknown uncertainties can be tackled by online learning and more accurate compensation. The Actor-NN is adopted for generating actions (regarded as compensation signals), and the Critic-NN is employed to evaluate the performed actions (regarded as to monitor and assess the Actor-NN performance, including the control performance). Moreover, a self-learning disturbance observer with learning ability is designed to estimate the external disturbance. On the basis of the backstepping control technology, the event-triggered control method and the smooth approximation of input saturation nonlinearity, an improved event-triggered constrained control strategy is presented using reinforcement learning technique, and the rigorous theoretical proofs of the closed-loop system stability and the avoidance of Zeno behavior are presented. The application for the quadrotor unmanned aerial vehicle validates the effectiveness of the developed event-triggered control approach.