Event-triggered adaptive neural backstepping control for nonstrict-feedback nonlinear time-delay systems

Abstract

This paper investigates the adaptive tracking control problem for a class of nonstrict-feedback nonlinear time-delay systems under event-triggered mechanism. The approach of neural network(NN) approximation is extended to nonstrict-feedback nonlinear systems. The adaptive NN controller is designed via backstepping technique and event-triggered mechanism. By the above techniques, the global Lipschitz condition of the unknown nonlinear function is released and the assumption of input-to-state stability (ISS) with respect to the measurement error is removed. It is shown that the proposed control scheme can guarantee all signals in closed-loop systems to be semi-global uniformly ultimately bounded (SGUUB), while the tracking error can converge to a small neighborhood of the origin. Finally, two examples are given to clarify the feasibility and effectiveness of the proposed design methodology.