Adaptive Neural Control Design for Strict-Feedback Time-Delay Nonlinear Systems Based on Fast Finite-Time Stabilization: A Case Study of Synchronous Generator Systems

• Published in: Journal of advanced computational intelligence and intelligent informatics Vol. 28; no. 6; pp. 1231 - 1239
• Main Authors: Wang, Honghong, Chen, Bing, Lin, Chong, Xu, Gang
• Format: Journal Article
• Language: English
• Published: Tokyo Fuji Technology Press Co. Ltd 01.11.2024
• Subjects: Adaptive control; Approximation; Case studies; Closed loops; Control algorithms; Control methods; Control stability; Controllers; Electrical engineering; Feedback control; Functionals; Methods; Neural networks; Nonlinear control; Nonlinear systems; Numerical models; Stability criteria; Synchronous machines
• ISSN: 1343-0130; 1883-8014
• DOI: 10.20965/jaciii.2024.p1231

This study aims to investigate the finite-time control problem for a class of strict-feedback time-delay nonlinear systems with unknown functions. The control design is based on a fast finite-time practical stability criterion. Unknown nonlinear functions can be estimated using the universal approximation performance of neural networks. Finite-time control design is performed using adaptive backstepping technology. By performing closed-loop stability analyses and choosing appropriate Lyapunov–Krasovskii functionals, all signals in a closed-loop system can be bounded within a finite time. Subsequently, the proposed control method can be applied for the excitation control of synchronous generators. The effectiveness of the proposed method is verified using a numerical model of a single-machine power system.