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JRM Vol.20 No.1 pp. 171-177
doi: 10.20965/jrm.2008.p0171
(2008)

Paper:

A New Adaptive Control Scheme Using Dynamic Neural Networks

Khaled Nouri*, Rached Dhaouadi**, and Naceur Benhadj Braiek*

*Laboratoire d’Etude et Commande Automatique de Processus, Ecole Polytechnique de Tunisie, BP, 748 La Marsa, 2078 Tunisie

**Department of Electrical Engineering, School of Engineering, American University of Sharjah, P.O. Box 26666, Sharjah, UAE

Received:
September 28, 2006
Accepted:
January 31, 2007
Published:
February 20, 2008
Keywords:
recurrent neural network, nonlinear system, motor drive, Kalman filter, inverse model
Abstract

A new adaptive neuro-control structure is proposed for the speed control of a nonlinear motor drive system and the compensation of the nonlinearities. A dynamic artificial neural network is used for the on-line adaptive control of the nonlinear motor drive system with high static and Coulomb friction. The neural network is first trained off-line to learn the inverse dynamics of the motor drive system using a layer decoupled extended Kalman filter algorithm. The proposed control scheme is validated experimentally on a dc motor drive system using a standard personal computer. The results obtained confirm the excellent tracking performance and disturbance rejection properties of the system.

Cite this article as:
Khaled Nouri, Rached Dhaouadi, and Naceur Benhadj Braiek, “A New Adaptive Control Scheme Using Dynamic Neural Networks,” J. Robot. Mechatron., Vol.20, No.1, pp. 171-177, 2008.
Data files:
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