电流最优的多变量静态神经网络开关磁阻电机控制

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Abstract Through the analysis of experimental results of prototyped switched reluctance motor (SRM), the effect of its speed, phase current, turn-on angle and turn-off angle on torque are introduced firstly. A SRM control system with optimal current mode is realized based on the foundation of its static multivariable neural network controller and the design of its training method to train the neural network secondly. To obtain training data of neural network efficiently, a variable step-size optimization method based on least square method is proposed. Current optimal control system can be realized to make output maximum torque by adjusting the turn-on and turn-off angle. Then the presented neutral controller is combined with the normal PID controller to achieve its ability of dynamic adjustment. Finally, the effectiveness of the proposed design is demonstrated through prototype SRM experiments.

Key words： Switched reluctance motor (SRM) neural network nonlinear control

Received: 02 March 2013 Published: 25 March 2014

PACS:

TM351

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	Articles by authors
	Zhang Yun
	Xu Yanliang
	Kong Hui
	Li Yuandong

Cite this article:

Zhang Yun,Xu Yanliang,Kong Hui等. Switched Reluctance Motor Control Based on Multivariable Neural Network with Optimal Phase Current[J]. Transactions of China Electrotechnical Society, 2013, 28(8): 250-258.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2013/V28/I8/250

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