Neural adaptive prescribed performance control for interconnected nonlinear systems with output dead zone

被引：16
作者：
Du, Peihao [1 ]
Zhou, Qi [2 ,3 ]
Liang, Hongjing [4 ]
机构：
[1] Bohai Univ, Sch Math & Phys, Jinzhou, Peoples R China
[2] Guangdong Univ Technol, Sch Automat, Guangzhou, Guangdong, Peoples R China
[3] Guangdong Univ Technol, Guangdong Prov Key Lab Intelligent Decis & Cooper, Guangzhou, Guangdong, Peoples R China
[4] Bohai Univ, Coll Engn, Jinzhou 121013, Peoples R China
来源：
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 2020年 / 30卷 / 03期
基金：
中国国家自然科学基金;
关键词：
adaptive control; interconnected nonlinear systems; neural networks; prescribed performance; unknown dead zone outputs; TIME-VARYING DELAY; DECENTRALIZED CONTROL; TRACKING CONTROL; FEEDBACK CONTROL; FUZZY CONTROL;
D O I：
10.1002/rnc.4802
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper is concerned with the neural-based decentralized adaptive control for interconnected nonlinear systems with prescribed performance and unknown dead zone outputs. In the controller design procedure, neural networks are employed to identify unknown auxiliary functions, and the control design obstacle caused by the output nonlinearity is resolved via introducing Nussbaum function. Then, a reliable neural decentralized adaptive control is developed through incorporating the backstepping method and the prescribed performance technique. In the light of Lyapunov stability theory, it is verified that the proposed control scheme can ensure that all the closed-loop signals are bounded, and can also guarantee that the tracking errors remain within a small enough compact set with the prescribed performance bounds. Finally, some simulation results are given to illustrate the feasibility of the devised control strategy.
引用
收藏
页码：999 / 1020
页数：22
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