Sensorless Control of Switched Reluctance Motor Based on ANFIS

Abstract

The accurate rotor position information is very important for high performance operation of switched reluctance motor (SRM). Traditionally, there is a mechanical rotor position sensor. But this reduces the reliability and increases cost and size of SRM. In order to overcome the disadvantage of mechanical rotor position sensor, a sensorless operation method of SRM based on adaptive network based fuzzy inference system (ANFIS) is developed in this paper. The rotor position can be estimated by using the unique relationship between rotor position, flux linkage and phase current. In this paper, the ANFIS is used to map this relationship. Among the sensorless position estimation method, approach based on fuzzy neural network (FNN) is one of the promising methods. By combining the benefits of artificial neural network (ANN) and fuzzy inference system (FIS) in a single model, the ANFIS shows characteristics of fast and accurate learning, the ability of using both linguistic information and data information and good generalization capability. For its antecedents are fuzzy sets, the noise in the input signals can be restrained. This approach shows a characteristic of robustness. For its consequent is in linear function of input variables, it has a simple structure and low computation complexity. So, it is well suited to be applied on-line. Applying it to the rotor position estimation, a high accuracy and robust sensorless rotor position estimator is presented. The experimental results proved the effectiveness of the proposed method.

This work is supported by Natural Science Foundation of Tianjin.