Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/56719
Title: Multiobjective optimization of current waveforms for switched reluctance motors by genetic algorithm
Authors: Xu, J.-X. 
Panda, S.K. 
Zheng, Q.
Keywords: Genetic algorithm
Multiobjective optimization
Switched reluctance motors
Torque-sharing function
Issue Date: 2004
Source: Xu, J.-X.,Panda, S.K.,Zheng, Q. (2004). Multiobjective optimization of current waveforms for switched reluctance motors by genetic algorithm. International Journal of Modelling and Simulation 24 (3) : 161-167. ScholarBank@NUS Repository.
Abstract: 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.
Source Title: International Journal of Modelling and Simulation
URI: http://scholarbank.nus.edu.sg/handle/10635/56719
ISSN: 02286203
Appears in Collections:Staff Publications

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