Adaptive neural tracking control for switched nonlinear systems with state quantization q

被引:19
|
作者
Zeng, Danping [1 ,2 ]
Liu, Zhi [1 ,2 ]
Chen, C. L. Philip [3 ]
Zhang, Yun [1 ,2 ]
机构
[1] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
[2] Guangdong HongKong Macao Joint Lab Smart Discrete, Guangzhou 510006, Peoples R China
[3] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Adaptive neural control; Switched nonlinear systems; State quantization; Arbitrary switching; Backstepping; FEEDBACK TRACKING; BACKSTEPPING CONTROL; STABILIZATION; INPUT; STABILITY;
D O I
10.1016/j.neucom.2021.02.083
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this study, an adaptive neural tracking control problem for uncertain switched nonlinear systems with state quantization under arbitrary switching is investigated. A command-filtered backstepping control design strategy is implemented to overcome the difficulty that the time derivate of common virtual con-trol signals cannot be well defined. By deriving the closed-loop system quantized errors bounded, quan-tized states can be used to the control design and unquantized states can be applied to the stability analysis. And then, an adaptive neural tracking control approach for switched nonlinear systems with state quantization via common Lyapunov function is proposed, which guarantees that all the signals in the closed-loop system remain semiglobal uniform ultimate boundedness and the genuine output of sys-tem can well track the reference trajectory. Finally, the proposed method is demonstrated by two simu-lation results. (c) 2021 Published by Elsevier B.V.
引用
收藏
页码:392 / 404
页数:13
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