Event-triggered ADP-based tracking controller for partially unknown nonlinear uncertain systems with input and state constraints

Abstract

This paper addresses the robust tracking control problem for nonlinear systems with unmatched uncertainties and partially unknown dynamics while also taking into account the input and state constraints. An event-triggered ADP framework is utilized to tackle this issue. Initially, an identifier neural network (NN) is designed to estimate the unknown system dynamics. Next, an augmented system is constructed using the reference trajectory and tracking error. The uncertainty is then divided into matched and unmatched components, converting the tracking control problem into an optimal regulation problem for an auxiliary system. A novel event-triggered safe HJB equation is developed by integrating a control barrier function (CBF) and a nonquadratic term within the cost function to enforce the safety constraints. A critic NN is utilized to solve this safe HJB equation. The controller is updated based on a triggering rule formulated using the Lyapunov approach. Lyapunov stability theory is applied to demonstrate that the closed-loop system is stable and that the identifier network and the critic network parameters remain uniformly ultimately bounded (UUB) under constraints and disturbances. The effectiveness of the proposed theoretical approach is validated using a simulation example.