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JACIII Vol.28 No.6 pp. 1231-1239
doi: 10.20965/jaciii.2024.p1231
(2024)

Research Paper:

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

Honghong Wang*,†, Bing Chen**, Chong Lin**, and Gang Xu***

*College of Electrical Engineering, Qingdao University
No.308 Ningxia Road, Shinan District, Qingdao, Shandong 266071, China

Corresponding author

**Institute of Complexity Science, Qingdao University
No.308 Ningxia Road, Shinan District, Qingdao, Shandong 266071, China

***School of Mechanical and Electrical Engineering, Weifang Vocational College
No.8029 Dongfeng East Street, Kuiwen District, Weifang, Shandong 261041, China

Received:
March 8, 2024
Accepted:
May 28, 2024
Published:
November 20, 2024
Keywords:
practical finite-time stability, neural adaptive control, backstepping design, synchronous generator, excitation control
Abstract

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.

Cite this article as:
H. Wang, B. Chen, C. Lin, and G. Xu, “Adaptive Neural Control Design for Strict-Feedback Time-Delay Nonlinear Systems Based on Fast Finite-Time Stabilization: A Case Study of Synchronous Generator Systems,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.6, pp. 1231-1239, 2024.
Data files:
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Last updated on Dec. 13, 2024