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
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.
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