[KSCI] Korea Science Citation Index Service

Load Variation Compensated Neural Network Speed Controller for Induction Motor Drives

Oh, Won-Seok (Dept. of Elec. Eng., Yuhan College)
Cho, Kyu-Min (Dept. of Info. And Comm. Eng., Yuhan College)
Kim, Young-Tae (Dept. of Elec. Eng., Wonju College)
Kim, Hee-Jun (Division of Elec. And Comp. Eng., Hanyang University)

Publication Information

KIEE International Transaction on Electrical Machinery and Energy Conversion Systems / v.3B, no.2, 2003 , pp. 97-102 More about this Journal

Abstract

In this paper, a recurrent artificial neural network (RNN) based self-tuning speed controller is proposed for the high-performance drives of induction motors. The RNN provides a nonlinear modeling of a motor drive system and could provide the controller with information regarding the load variation system noise, and parameter variation of the induction motor through the on-line estimated weights of the corresponding RNN. Thus, the proposed self-tuning controller can change the gains of the controller according to system conditions. The gain is composed with the weights of the RNN. For the on-line estimation of the RNN weights, an extended Kalman filter (EKF) algorithm is used. A self-tuning controller is designed that is adequate for the speed control of the induction motor The availability of the proposed controller is verified through MATLAB simulations and is compared with the conventional PI controller.

Keywords

EKF; induction motor; neural network; load variation; self-tuning; on-line estimation;

Citations & Related Records

Reference

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