Stabilizing switching control for nonlinear system based on quasi‐ARX RBFN model | Zendy

Wang Lan | Zendy; Cheng Yu | Zendy; Hu Jinglu | Zendy

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Stabilizing switching control for nonlinear system based on quasi‐ARX RBFN model

Author(s) -

Wang Lan,

Cheng Yu,

Hu Jinglu

Publication year - 2012

Publication title -

ieej transactions on electrical and electronic engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.254

H-Index - 30

eISSN - 1931-4981

pISSN - 1931-4973

DOI - 10.1002/tee.21745

Subject(s) - control theory (sociology) , nonlinear system , robustness (evolution) , fuzzy logic , fuzzy control system , radial basis function network , computer science , control engineering , stability (learning theory) , engineering , artificial neural network , control (management) , radial basis function , artificial intelligence , machine learning , biochemistry , chemistry , physics , quantum mechanics , gene

In this paper, a fuzzy switching adaptive control approach is presented for nonlinear systems. The proposed fuzzy switching adaptive control law is composed of a quasi‐ARX radial basis function network (RBFN) prediction model and a fuzzy switching mechanism. The quasi‐ARX RBFN prediction model consists of two parts: a linear part used for a linear controller to ensure boundedness of the input and output signals; and an RBFN nonlinear part used to improve control accuracy. By using the fuzzy switching scheme between the linear and nonlinear controllers to replace the 0/1 switching, it can realize a better balance between stability and accuracy. Theoretical analysis and simulation results show the effectiveness of the proposed control method on the stability, accuracy, and robustness. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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