Multiobjective Optimization of Current Waveforms for Switched Reluctance Motors by Genetic Algorithm

J.-X. Xu, S.K. Panda, and Q. Zheng


Genetic algorithm, switched reluctance motors, torque-sharing function, multiobjective optimization


In this article a genetic algorithm (GA) is employed to determine the desired current waveforms for switched reluctance motors (SRM) through generating appropriate reference phase torques for a given desired torque using torque-sharing function. The objective is to yield smoother phase current waveforms in general, and achieve minimum phase current variations in particular. This problem is formulated into a multiobjective optimization task with certain constraints. Due to the highly nonlinear relationship between the SRM torque and current, this optimization task is an NP-hard problem. To deal with the difficulty, the problem is further coded so that a GA can be applied to facilitate the search of global minimum. Simulation results verify the effectiveness of the proposed method.

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