Design of adaptive backstepping dynamic surface control method with RBF neural network for uncertain nonlinear system

被引：83
|
作者：
Shi, Xiaoyu [1 ]
Cheng, Yuhua [1 ]
Yin, Chun [1 ]
Huang, Xuegang [2 ]
Zhong, Shou-ming [3 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Sichuan, Peoples R China
[2] China Aerodynam Res & Dev Ctr, Hyperveloc Aerodynam Inst, Mianyang 621000, Peoples R China
[3] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China
来源：
NEUROCOMPUTING | 2019年 / 330卷
关键词：
RBF neural network; Dynamic surface control; Nonlinear System; Adaptive control law; SLIDING MODE CONTROL; EXTREMUM SEEKING; CONTROL STRATEGY; TRACKING; SYNCHRONIZATION; ALGORITHM;
D O I：
10.1016/j.neucom.2018.11.029
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper develops an adaptive backstepping dynamic surface control method with RBF Neural Network for a class of nonlinear system under extra disturbances. The considered RBF Neural Network based on adaptive control is applied to approximate the unknown smooth function arbitrarily. The "explosion of the complexity" is eliminated by utilizing the dynamic surface control technique. The Lyapunov function is employed to verify the globally asymptotically stability of the control nonlinear system. Four examples were given to show that the novel control method can not only tracking the expected trajectory very well but also has a better approximation capability for various complex unknown smooth function under disturbances. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：490 / 503
页数：14
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