Direct torque control of adaptive asynchronous motor based on Hopfield neural network

被引：0

作者：

Fu, Xing-Feng ^{[1
,2
]}

Luo, Yu-Tao ^{[1
,2
]}

Zhou, Si-Jia ^{[1
,2
]}

Yang, Yong ^{[1
,2
]}

机构：

[1] School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China

[2] Guangdong Provincial Key Laboratory of Automotive Engineering, Guangzhou 510640, China

来源：

Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2008年 / 36卷 / 10期

关键词：

Adaptive control systems - Electric machine control - Speed - Torque - Hopfield neural networks - Stators - Torque control - Speed control;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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 Hopfield 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.

引用

页码：61 / 66