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The switched reluctance motor (SRM) is a powerful candidate for many domestic and industrial applications. However, the double salient structure and discrete commutation process make it very difficult to acquire the analytical model of SRM. The performance optimization of SRM is achieved mainly based on the observation and analysis of its static magnetization characteristics. This paper presents multi-objective optimization of SRM's control parameters for optimum motor operation over wide range of speeds. The optimization aims to achieve the maximum torque production with the lowest copper loss. A searching algorithm is developed to find the base values as they vary for each operating point. The objective function is calculated using a dynamic/actual simulation model of SRM. For a highly trusted model of SRM, the static magnetization characteristics of tested 8/6 SRM are measured experimentally. Then, the measured data are used to build the model in a MATLAB/Simulink environment. The proposed control is implemented using an artificial neural network (ANN). A series of simulations and experimental results are obtained to show the feasibility of the proposed control.

Optimum Control Parameters of Switched Reluctance Motor for Torque Production Improvement over the Entire Speed Range