Modeling of Switched Reluctance Motor Based on GA Optimized T-S Type Fuzzy Logic

Abstract

Flux linkage of switch reluctance motor (SRM) is in nonlinear function of both rotor position and phase current. Establishing this nonlinear mapping is the basis of computing the mathematical equations of SRM accurately. In this paper, the Takagi-Sugeno (T-S) type fuzzy logic is employed to develop the nonlinear model of SRM. By taking advantage of the benefit of T-S type fuzzy logic inference, the T-S type fuzzy logic based model of SRM has a simple structure, less training epoch, fast computational speed and characteristics of robustness. In order to get a high precision, the parameters of T-S type fuzzy logic based model of SRM should be optimized. For there is no derivative information available, the conventional optimal method, such as steepest gradient decent optimization method, is hard to be used to optimize the parameters of the T-S type fuzzy logic. In this paper, genetic algorithm (GA) is used to optimize the parameters of the proposed model. GA is an optimization technique that performs a parallel, stochastic, but directed search to evolve the most fit population and it do not relay on computing local derivatives to guide the search process. Compared with the training data and generalization test data, the output data of the developed model are in good agreement with those data. The simulated current wave is also in good agreement with the measured current wave. This proves that the model developed in this paper has high accuracy, strong generalization ability, fast computation speed and characteristics of robustness.