Torque ripple minimization in switched reluctance motors using fuzzy-neural network inverse learning control

Abstract

The purpose of this paper is the development of fuzzy-neural network (FNN) inverse learning control algorithms for torque-ripple minimization of SRMs. The approach consists of two FNN modules, which spare the same weight values. The learning FNN module is used to adjust the weight values on-line based on observations of the SRMs' (T-i-/spl theta/) input-output relationship in order to form an approximate dynamic inverse model i(T, /spl theta/) of SRMs. The controlling FNN module is used to predict the SRMs phase current waveforms required to follow a desired torque command. Detailed simulation results show good response characteristics for a four-phase SRM.