基于DSP的开关磁阻电机磁链特性检测与神经网络建模

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Abstract A wavelet neural network(WNN) model of flux linkage characteristic based on measured magnetization curves is presented for switched reluctance motors(SRM). A flux linkage measuring installation is setup by using a digital signal processor(DSP). The magnetization curves at different rotor positions are obtained by calculating flux linkage using a discrete method on an upper level computer. Then, a feed-forward neural network(FFNN) model of flux linkage characteristic is proposed. The commonly used Mexican hat wavelet is chosen as the activation function for hidden-layer neurons, and its parameters are determined by training the network with the descend gradient method. Simulation indicates that the proposed model has reduced error and improved generalization property. It shows some prospect on optimizing online control of SRM.

Key words： Switched reluctance motor flux linkage characteristic wavelet neural network DSP TMS320F2812

Received: 24 March 2008 Published: 07 March 2014

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TM351

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	Xue Mei
	Xia Changliang
	Wang Huimin
	Xie Ximing

Cite this article:

Xue Mei,Xia Changliang,Wang Huimin等. Flux Linkage Characteristic Measurement Based on DSP and Artificial Neural Network Modeling for Switched Reluctance Motor[J]. Transactions of China Electrotechnical Society, 2011, 26(2): 68-73.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2011/V26/I2/68

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