NDLI: RBF neural networks-based robust adaptive tracking control for switched uncertain nonlinear systems

Content Provider	Springer Nature Link
Author	Yu, Lei Fei, Shumin Li, Xun
Copyright Year	2012
Abstract	In this paper, a robust adaptive H∞ control scheme is presented for a class of switched uncertain nonlinear systems. Radical basis function neural networks (RBF NNs) are employed to approximate unknown nonlinear functions and uncertain terms. A robust H∞ controller is designed to enhance robustness due to the existence of the compound disturbance which consists of approximation errors of the neural networks and external disturbance. Adaptive neural updated laws and switching signals are deducted from multiple Lyapunov function approach. It is proved that with the proposed control scheme, the resulting closed-loop switched system is robustly stable and uniformly ultimately bounded (UUB) such that good capabilities of tracking performance is attained and H∞ tracking error performance index is achieved. A practical example shows the effectiveness of the proposed control scheme.
Starting Page	437
Ending Page	443
Page Count	7
File Format	PDF
ISSN	15986446
Journal	International Journal of Control, Automation and Systems
Volume Number	10
Issue Number	2
e-ISSN	20054092
Language	English
Publisher	Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
Publisher Date	2012-04-17
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Radical basis function neural networks robust adaptive H∞ control switched uncertain nonlinear systems tracking performance Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications

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