Journal of South China University of Technology (Natural Science Edition) ›› 2008, Vol. 36 ›› Issue (10): 61-66.

• Traffic & Transportation Engineering • Previous Articles     Next Articles

Direct Torque Control of Adaptive Asynchronous Motor Based on Hopfield Neural Network

Fu Xing-feng 1.2  Luo Yu-tao 1.2  Zhou Si-jia 1.2  Yang Yong 1.2   

  1. 1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China; 2. Guangdong Provincial Key Laboratory of Automotive Engineering, Guangzhou 510640, Guangdong, China
  • Received:2007-10-26 Revised:2007-12-09 Online:2008-10-25 Published:2008-10-25
  • Contact: 符兴锋（1977-），男，博士生，主要从事EV、HEV电子控制和电动机智能控制研究． E-mail:fxf1000@163．com
  • About author:符兴锋（1977-），男，博士生，主要从事EV、HEV电子控制和电动机智能控制研究．
  • Supported by:

    国家自然科学基金资助项目（50605020）;广东省科技攻关项目（2006A10501001）

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Abstract:

In order to enhance the performances of the traditional direct torque control system of the asynchronous motor that are restricted by the great ripples of the electromagnetic torque, the stator flux and the stator current at a low steady motor speed, an improved direct torque control method is proposed based on the I4opfield neural network and the dynamic mathematical model of the asynchronous motor. It is found that the proposed method not only effectively reduces the ripples of the electromagnetic torque, the stator flux and the stator current but also enhances the low-speed performance of the speed control system. Modeling and Simulation results indicate that the proposed method is of excellent robustness.

Key words: asynchronous motor, speed control system, Hopfield neural network, direct torque control