Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32468
Dynamic Fuzzy-Neural Network Controller for Induction Motor Drive

Authors: M. Zerikat, M. Bendjebbar, N. Benouzza


In this paper, a novel approach for robust trajectory tracking of induction motor drive is presented. By combining variable structure systems theory with fuzzy logic concept and neural network techniques, a new algorithm is developed. Fuzzy logic was used for the adaptation of the learning algorithm to improve the robustness of learning and operating of the neural network. The developed control algorithm is robust to parameter variations and external influences. It also assures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the designed controller of induction motor drives which considered as highly non linear dynamic complex systems and variable characteristics over the operating conditions.

Keywords: Induction motor, fuzzy-logic control, neural network control, indirect field oriented control.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2349


[1] C.M. Liaw, Y.S. Kung and C.M. Wu Design and implementation of a high-performance field-oriented induction motor drive. IEEE Trans. Ind. Electron.,vol.38,4,pp.275-282,1991.
[2] M.A. Wishart and R.G. Harley. Identification and control of induction machines using artificial neural networks. IEEE Trans. Ind. Applicat., vol.31,pp.612-619, 1995.
[3] Y.S. Kung, C.M. Liaw and M.S. Ouyang. Adaptive speed control for induction motor drives using neural networks. IEEE Trans. Ind. Electron. Vol.42,1,pp.25-32, 1995.
[4] T.C. Chen and T.T. Sheu. Robust speed-controlled induction motor drive based on model reference with neural networks. Inter. Journ. Of Knowledge Based Intelligent Engineering System. Vol.3,3.pp.162-171, 1992.
[5] Levin and K.S. Narendra. Control dynamics systems using neural networks: Controllability and Stabilization . IEEE Trans. on Neural Networks, Vol.4,No.2,March 1993.
[6] K.S. Narendra and K. Parthasarathy. Identification and control for dynamical systems using neural networks . IEEE Trans. Neural Networks, NN-1,1,4-27, 1990.
[7] Y. Edward, Y. Ho and C. Paresh .Control dynamics of speed drive systems using sliding mode controllers with integral compensation . IEEE Trans. On Industry Applications, Vol.,27, No.5, Sept-Oct. 1991.
[8] Jie Zhang and T.H. Burton. New approach to field orientation control of CSI induction motor drive. IEE Proceedings, Vol.135,Pt. B. No.1; January 1988.
[9] B. Burton and F. Kamran. Identification and control of induction motor stator currents using fast on-line random training of neural networks . IEEE Trans. on Industry Applications, Vol.33,No.3,May-June,1997.
[10] D. Nguyen and B. Widrow. Improving the learning speed of two layer neural networks by choosing initial values of adaptive weights. Proc. Int. Joint Conf. Neural Networks, San Diego, CA, pp.21-26, july, 1990.
[11] G.C.D. Souza, B.K. Bose and J.G. Cleland. Fuzzy logic based on-line efficiency optimization control of an indirect vector controlled induction motor drive, IEEE-IECOM Conf. Maui, HI, pp.1168-1174, november 1993.
[12] G.C.D. Souza and B.K. Bose. A fuzzy set theory based control of a phase controlled converter DC machine drive, IEE-IAS Annu. Meeting Conf. Rec., Dearborn, MI, pp.854-861, October 1991.
[13] Q. Li , S.K. Tso and A.N. Poo. PID tuning using neural networks, Intelligent Automation and Control TSI Press Inc., USA, pp.461-465, 1998.