A New Adaptive Control Scheme Using Dynamic  Neural  Networks

Khaled Nouri; Rached Dhaouadi; Naceur Benhadj Braiek

doi:10.20965/jrm.2008.p0171

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JRM Vol.20 No.1 pp. 171-177

doi: 10.20965/jrm.2008.p0171

(2008)

Paper:

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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:

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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References

[1] G. L. Plett, “Adaptive inverse control of linear and nonlinear systems using dynamic neural networks,” IEEE Trans. on Neural Networks, 14, pp. 360-376, 2003.
[2] S. H. Lane and R. F. Stengel, “Flight control design using nonlinear inverse dynamics,” Automatica, 24, pp. 471-483, 1988.
[3] X. M. Ren, A. B. Rad, P. T. Chan, and W. L. Lo, “Identification and control of continuous-time nonlinear systems via dynamic neural networks,” IEEE Transactions on Industrial Electronics, 50, pp. 478-486, 2003.
[4] I. Kanellakopoulos, P. V. Kokotovic, and R. Mariano, “An extended direct scheme for robust adaptive nonlinear control,” Automatica, 34, pp. 247-255, 1991.
[5] D. Enns, D. Bugajski, R. Hendrick, and G. Stein, “Dynamic inversion: An evolving methodology for flight control design,” Int. J. Contr., 59, pp. 71-91, 1994.
[6] L. Yan and C. J. Li, “Robot learning control based on recurrent neural networks,” Journal of Robot. Syst., 14, pp. 26-33, 1997.
[7] G. V. Puskorius and L. A. Feldkamp, “Decoupled extended Kalman filter training of feed-forward layered neural networks,” Proc. IEEE International Joint Conference on Neural Networks, Vol.1, pp. 771-777, 1991.
[8] S. Singhal and L. Wu, “Training multi-layer perceptions with the extended Kalman algorithm,” In Advances in Neural Information Proceeding Systems 1, Vol.1, pp. 133-140, 1988.
[9] K. Nouri, R. Dhaouadi, and N. B. Braiek, “A dynamic neural network based decoupled extended Kalman filter control applying to motor drive system,” 17ème Congrés Mondial IMACS, Calcul Scientifique, Mathématique Appliquées et Simulation, 2005.
[10] P. J. Werbos, “Back propagation through time: what it does and how to do it?,” Proc. of the IEEE, Vol.78, pp. 1550-1560, 1990.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] G. L. Plett, “Adaptive inverse control of linear and nonlinear systems using dynamic neural networks,” IEEE Trans. on Neural Networks, 14, pp. 360-376, 2003.

[2] [2] S. H. Lane and R. F. Stengel, “Flight control design using nonlinear inverse dynamics,” Automatica, 24, pp. 471-483, 1988.

[3] [3] X. M. Ren, A. B. Rad, P. T. Chan, and W. L. Lo, “Identification and control of continuous-time nonlinear systems via dynamic neural networks,” IEEE Transactions on Industrial Electronics, 50, pp. 478-486, 2003.

[4] [4] I. Kanellakopoulos, P. V. Kokotovic, and R. Mariano, “An extended direct scheme for robust adaptive nonlinear control,” Automatica, 34, pp. 247-255, 1991.

[5] [5] D. Enns, D. Bugajski, R. Hendrick, and G. Stein, “Dynamic inversion: An evolving methodology for flight control design,” Int. J. Contr., 59, pp. 71-91, 1994.

[6] [6] L. Yan and C. J. Li, “Robot learning control based on recurrent neural networks,” Journal of Robot. Syst., 14, pp. 26-33, 1997.

[7] [7] G. V. Puskorius and L. A. Feldkamp, “Decoupled extended Kalman filter training of feed-forward layered neural networks,” Proc. IEEE International Joint Conference on Neural Networks, Vol.1, pp. 771-777, 1991.

[8] [8] S. Singhal and L. Wu, “Training multi-layer perceptions with the extended Kalman algorithm,” In Advances in Neural Information Proceeding Systems 1, Vol.1, pp. 133-140, 1988.

[9] [9] K. Nouri, R. Dhaouadi, and N. B. Braiek, “A dynamic neural network based decoupled extended Kalman filter control applying to motor drive system,” 17ème Congrés Mondial IMACS, Calcul Scientifique, Mathématique Appliquées et Simulation, 2005.

[10] [10] P. J. Werbos, “Back propagation through time: what it does and how to do it?,” Proc. of the IEEE, Vol.78, pp. 1550-1560, 1990.

A New Adaptive Control Scheme Using Dynamic Neural Networks

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

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