Dynamic surface adaptive RBF neural network control for a class of non-linear uncertain systems

被引：1
|
作者：
Zhang, Dong [1 ]
Wang, Hongfei [2 ]
Shan, Shaolong [2 ]
Wang, Chunbo [1 ]
机构：
[1] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
[2] Tangshan Vocat & Tech Coll, Tangshan, Peoples R China
来源：
ELECTRONICS LETTERS | 2023年 / 59卷 / 23期
基金：
国家重点研发计划;
关键词：
adaptive control; neural net architecture; LAW;
D O I：
10.1049/ell2.13055
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
An adaptive neural network dynamic surface control method for strict feedback non-linear systems with model uncertainty is proposed in this paper. The uncertain parts are approached by RBF neural network; meanwhile, the virtual controller is designed to make system stable according to dynamic surface control. The method of Lyapunov is used to prove the stability and convergence of the system. The simulation results prove the feasibility of this controller and prove that the controller has an advantage to approach the uncertain non-linear system and make system have well convergence and traceability. A simplified adaptive neural network dynamic surface control method for strict feedback non-linear systems with model uncertainty is proposed in this paper.image
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页数：4
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