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

摘要

This paper investigates the adaptive tracking control problem for a class of nonstrict-feedback nonlin- ear time-delay systems under event-triggered mechanism. The approach of neural network(NN) approx- imation 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. (c) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.