Optimal Event-Triggered Neural Learning Tracking Control for Pneumatic Muscle Antagonistic Joint With Asymmetric Constraints

In this article, an event-triggered adaptive dynamic programming (ETADP) method is proposed for the robust optimal tracking control problem of an uncertain continuous-time nonlinear system with asymmetric input constraints and external disturbances. First, an event-triggered integral reinforcement learning (ETIRL) algorithm is proposed to address the zero-sum game problem for partially unknown dynamical systems. A triggering condition with an interference attenuation level is designed to optimize the process. Second, a single-critic neural network structure is employed to approximate the optimal control law. The control and disturbance laws are updated non-periodically by the event-triggered. The asymptotic stability of the closed-loop system and the uniform ultimate boundedness (UUB) of the critic neural network weights are proven theoretically using the Lyapunov stability theorem. Finally, the feasibility of the algorithm is assessed through pneumatic muscle antagonistic joint experiments.