基于自适应模糊神经网络的开关磁阻电动机建模与仿真

由于开关磁阻电动机磁链特性的高度非线性,在常规的解析建模方法中,由近似磁链模型导出的转矩特性的误差较大.在实测开关磁阻电动机静态磁链曲线的基础上,计算获得开关磁阻电动机的矩角特性,并且将自适应的模糊神经网络用于开关磁阻电动机的建模中,获得了良好的效果.仿真结果表明,此法对于分析开关磁阻电动机及其驱动系统在各种控制方式下的运行性能具有一定的参考价值.

Modeling and Simulation of Switched Reluctance Motor Based on Adaptive Fuzzy-Neural Networks

Because the characteristic of flux linkage in the switche reluctance motor(SRM) is highly non-linear, in the analytical model, the toque-angle characteristic which leading out from the flux linkage model may bring very heavy error. In this paper, based on surveying static curve or SRM flux linkage, obtaining the torque-angle characteristic through calculating the flux linkage, using adaptive fuzzy-neural network to SRM modeling, then the good effect was obtained The simulation result shows that this method has certain reference value to analyze the running performance of SRM and its driving system under different control modes.