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2006 | 16 | 2 | 219-232
Tytuł artykułu

Neural network-based MRAC control of dynamic nonlinear systems

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents direct model reference adaptive control for a class of nonlinear systems with unknown nonlinearities. The model following conditions are assured by using adaptive neural networks as the nonlinear state feedback controller. Both full state information and observer-based schemes are investigated. All the signals in the closed loop are guaranteed to be bounded and the system state is proven to converge to a small neighborhood of the reference model state. It is also shown that stability conditions can be formulated as linear matrix inequalities (LMI) that can be solved using efficient software algorithms. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation results are presented to show the effectiveness of the approach.
Rocznik
Tom
16
Numer
2
Strony
219-232
Opis fizyczny
Daty
wydano
2006
otrzymano
2005-09-01
poprawiono
2006-04-16
Twórcy
  • Electrical Engineering Institute, Oum El-Bouaghi University, 04000 Oum El-Bouaghi, Algeria
  • Electronic Department, Constantine University, 25000 Constantine, Algeria
  • Electrical Engineering Institute, Oum El-Bouaghi University, 04000 Oum El-Bouaghi, Algeria
Bibliografia
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  • Patino H.D. and Liu D. (2000): Neural network-based model reference adaptive control system. - IEEE Trans. Syst. Man Cybern., Vol. 30, No. 1,pp. 198-204.
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  • Spooner J.T. and Passino K.M. (1996): Stable adaptive control using fuzzy systems and neural networks. - IEEE Trans. Neural Netw., Vol. 4,No. 3, pp. 339-359.
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Typ dokumentu
Bibliografia
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bwmeta1.element.bwnjournal-article-amcv16i2p219bwm
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