Neural Network-Based Adaptive Finite-Time Command-Filter Control for Nonlinear Systems With Input Delay and Input Saturation

Abstract

This study focuses on addressing the challenge of adaptive finite-time control for nonstrict-feedback nonlinear systems subject to input delay and saturation. Neural networks (NNs) are utilized to handle unknown nonlinear functions, and Padé approximation is employed to effectively manage input delay. To mitigate the issue of “explosion of complexity,” the command filter method is applied. By leveraging command filter technology and backstepping technique, an adaptive finite-time control scheme is developed using NN approximation. The proposed control scheme demonstrates that the closed-loop signals achieve semi-global practical finite-time stable (SGPFS), ensuring that the tracking error converges within a finite time to a small region around the origin. The effectiveness of the proposed scheme is validated through two simulation examples.