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

• Published in: International journal of adaptive control and signal processing Vol. 39; no. 1; pp. 231 - 243
• Main Author: Kharrat, Mohamed
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2025; Wiley Subscription Services, Inc
• Subjects: Adaptive control; Adaptive systems; Control systems; Delay; Feedback control systems; finite‐time stability; input delay; Neural networks; Nonlinear control; Nonlinear systems; Pade approximation; pendulum system; saturation; Simulation; Tracking errors
• ISSN: 0890-6327; 1099-1115
• DOI: 10.1002/acs.3936

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. This study addresses adaptive finite‐time control for nonstrict‐feedback nonlinear systems with input delay and saturation. Neural networks are employed to approximate unknown functions, while Padé approximation effectively handles input delay. The command filter method mitigates the “explosion of complexity.” By integrating command filtering with the backstepping technique, an adaptive control scheme is developed. The proposed approach ensures semi‐global practical finite‐time stability (SGPFS), with tracking errors converging to a small region near the origin. Its effectiveness is validated through two simulation examples.