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| Modeling of Switched Reluctance Motor Based on Pi-Sigma Fuzzy Neural Network |
| Xiu Jie, Xia Changliang, Wang Shiyu |
| Tianjin University Tianjin 300072 China |
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Abstract The high saturation of magnetic circuit and the doubly salient structure of switched reluctance motor (SRM) make the flux linkage a nonlinear function of rotor position and phase current. In this paper, the pi-sigma fuzzy neural network, which has the merit of T-S type fuzzy logic and neural network, is adopted to develop the nonlinear model of SRM and an adaptive learning rate training algorithm with momentum is applied. Relatively high precision model of SRM is implemented. It has simple structure, less training epoch and fast online calculation. The sampled phase current and rotor position are non-equally spaced. Thus a reasonable distribution of measured data is reached. The precision and generalization ability of the model are improved. Meanwhile the number of measured data is reduced. Compared with the measured data and generalization validate data, the output data of the model are in good agreement with those data. This proves that the precision of the model developed in this paper is relatively high. The model has the merits of relatively strong generalization ability, simple structure and fast calculation.
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Received: 19 February 2008
Published: 17 February 2014
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2009, Vol. 24 
