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Adaptive control of Wiener‐type nonlinear systems using neural networks
Author(s) -
Yamanaka Osamu,
Yoshizawa Naoto,
Ohmori Hiromitsu,
Sano Akira
Publication year - 1998
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/(sici)1520-6416(19980115)122:1<37::aid-eej5>3.0.co;2-t
Subject(s) - control theory (sociology) , nonlinear system , adaptive control , scheme (mathematics) , artificial neural network , bounded function , stability (learning theory) , adaptive system , computer science , type (biology) , mathematics , control (management) , artificial intelligence , physics , quantum mechanics , mathematical analysis , ecology , machine learning , biology
This paper proposes new adaptive control schemes with neural networks for Weiner‐type nonlinear systems which have output nonlinearity. First, by adopting a robust adaptive control law and a functional link network (FLN), we present an adaptive linearizing scheme as a primary step for a model reference control scheme, where the FLN compensates the output nonlinearity. Second, we analyze the stability of the adaptive linearizing scheme by using a robust adaptive control technique, and demonstrate that all of the parameters are bounded and that the boundedness of all of the signals in the closed loop is guaranteed under some reasonable conditions. Third, based on the linearizing scheme, we present a new direct model reference adaptive control scheme by choosing the reference output appropriately. The stability of the system is guaranteed under several conditions in a similar manner. Finally, we illustrate the effectiveness of the proposed scheme through some numerical examples. © 1998 Scripta Technica. Electr Eng Jpn, 122(1): 37–48, 1998