Identification of A Nonlinear Model of Switched Reluctance Motor Which Considers the Effects of Mutual Inductance and Magnetic Saturation

The paper presents a full nonlinear model for switched reluctance motor (SRM) based on artificial neural networks. The proposed neural network model consists of two different models, the forward one and the inverse one. The purpose of the forward model represents relationship between the flux linkage and torque as a functional dependence on the current stator and the position rotor. The inverse model is used to estimate current stator and flux linkage as a functional dependence on torque and rotor position. The tested SRM model is built on the basis of software simmechanic. The used neural networks is a multi-layered network and is trained with a feedforward algorithm. The nonlinear model proposed in this paper can be used to synthesize controllers for SRM in the following applications