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| Modeling and Simulation of Switched Reluctance Motor Based on RBFN-AFS |
| Ding Wen, Liang Deliang |
| State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong University Xi’an 710049 China |
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Abstract Considering the nonlinear, saturation and coupled magnetization, this paper presents a radial basis function network-based adaptive fuzzy system(RBFN-AFS)to model the switched reluctance motor (SRM) and predict the performance in SRM drive system. Based on the measured SRM’s flux linkage and torque data, the RBFN-AFS is designed to learn and train the electromagnetic characteristics knowledge for the SRM by using the hierarchically self-organizing learning(HSOL)algorithm to determine the minimum necessary number of rules and adjust the mean and variance vectors of individual hidden nodes as well as their weights. After training, the RBFN-AFS forms a very efficient mapping structure for the nonlinear characteristics of the SRM. Lastly, a RBFN-AFS current-dependent inverse flux linkage model and a RBFN-AFS torque model are used to simulate the dynamic performance of a 6/4 0.55kW SRM. The simulation results and experimental waveforms are reported to validate the proposed RBFN-AFS modeling method for SRM. It also provides the application of analysis and real time control for SRM.
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Received: 26 May 2008
Published: 17 February 2014
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2009, Vol. 24 
