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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.