Neural-network-based tracking Control for a Class of time-delay nonlinear systems with unmodeled dynamics

摘要

This paper is concerned with the tracking control problem of a class of non-strict-feedback nonlinear systems with unmodeled dynamics and time-delay. In the backstepping procedure, a dynamic signal is designed to handle the unmodeled dynamics and the Lyapunov-Krasovskii functions are applied to compensate for the effect of time delay. Meanwhile, a neural network-based approximator is used to approximate the unknown nonlinear functions in the system. It is proved by the theoretical analysis that the presented controller guarantees the semi-global boundedness of all signals in the closed-loop systems, and the output tracking error eventually converges to a small area around zero. Simulation results are presented to illustrate the validity of the proposed approach. (C) 2019 Elsevier B.V. All rights reserved.