Modeling of a 6/4 Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System

Abstract

The magnetic saturation and strong nonlinearity of switched reluctance machines (SRMs) makes it very difficult to derive a comprehensive mathematical model for the behavior of the machine. We propose a new method of modeling SRMs based on an adaptive neural fuzzy inference system (ANFIS). First, we use an indirect method to measure the static flux linkage and then use the co-energy method (via the principle of virtual displacement) to calculate the torque characteristics from data on flux linkage versus current and rotor position. A hybrid learning algorithm, which combines the back propagation algorithm and the linear least-squares estimation algorithm, identifies the parameters of the ANFIS. After training, the ANFIS flux linkage model and ANFIS torque model are in excellent agreement with experimental flux linkage measurements and the calculated torque data. Finally, we use an ANFIS current model and an ANFIS torque model to study SRM dynamic performance. The accuracy of the model was evaluated by comparison to laboratory measurements of the machine's current-speed and torque-speed characteristics. The model is quite accurate.