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


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

September 28, 2006
January 31, 2007
February 20, 2008
recurrent neural network, nonlinear system, motor drive, Kalman filter, inverse model
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:
K. Nouri, R. Dhaouadi, and N. Braiek, “A New Adaptive Control Scheme Using Dynamic Neural Networks,” J. Robot. Mechatron., Vol.20 No.1, pp. 171-177, 2008.
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Last updated on Apr. 19, 2024