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.
丁文, 梁得亮. 基于RBFN-AFS的开关磁阻电机非线性模型与动态仿真[J]. 电工技术学报, 2009, 24(9): 44-52.
Ding Wen, Liang Deliang. Modeling and Simulation of Switched Reluctance Motor Based on RBFN-AFS. Transactions of China Electrotechnical Society, 2009, 24(9): 44-52.
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