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Adaptive neural network output feedback stabilization of nonlinear non‐minimum phase systems
Author(s) -
Hoseini S. M.,
Farrokhi M.,
Koshkouei A. J.
Publication year - 2010
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.1111
Subject(s) - control theory (sociology) , nonlinear system , minimum phase , lyapunov function , adaptive control , observer (physics) , artificial neural network , computer science , controller (irrigation) , adaptive system , phase (matter) , control (management) , artificial intelligence , physics , quantum mechanics , agronomy , biology , chemistry , organic chemistry
This paper presents an adaptive output feedback stabilization method based on neural networks (NNs) for nonlinear non‐minimum phase systems. The proposed controller comprises a linear, a neuro‐adaptive, and an adaptive robustifying parts. The NN is designed to approximate the matched uncertainties of the system. The inputs of the NN are the tapped delays of the system input–output signals. In addition, an appropriate reference signal is proposed to compensate the unmatched uncertainties inherent in the internal system dynamics. The adaptation laws for the NN weights and adaptive gains are obtained using Lyapunov's direct method. These adaptation laws employ a linear observer of system dynamics that is realizable. The ultimate boundedness of the error signals are analytically shown using Lyapunov's method. The effectiveness of the proposed scheme is shown by applying to a translation oscillator rotational actuator model. Copyright © 2009 John Wiley & Sons, Ltd.