基于RBFN-AFS的开关磁阻电机非线性模型与动态仿真

针对开关磁阻电机(SRM)电磁特性存在饱和非线性、多变量、强耦合的特点,提出了一种基于径向基函数网络的自适应模糊系统(RBFN-AFS)建立SRM模型并进行动态仿真的新方法.该方法在实测SRM磁链和转矩特性的基础上,采用递阶自组织学习(HSOL)算法对RBFN-AFS网络进行学习训练,使网络从样本数据中估计出未知的模糊规则,并在学习训练过程中不断更新和修正网络隐层节点参数矢量和连接权值,最终实现磁链与转矩对转子位置角和相电流的非线性映射关系,与其他建模方法相比,该模型具有更快的计算速度和更好的泛化能力.将基于RBFN-AFS网络的电流-磁链和转矩模型应用于SRM调速系统的动态仿真分析中,通过仿真与实验比较,此方法能够很好地预测SRM的动态和稳态运行特性.这种基于RBFN-AFS的建模方法为实现SRM的各项性能分析和各种实时控制提供了一种新的思路.

Modeling and Simulation of Switched Reluctance Motor Based on RBFN-AFS

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.