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

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-θ) input-output relationship in order to form an approximate dynamic inverse model i(T, θ) 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.