Torque ripple minimization of a switched reluctance motor by using genetic fuzzy logic

Torque ripple minimization has been investigated by using with genetic fuzzy logic algorithm of switched reluctance motor (SRM) drive. Torque ripple minimization has been utilized to enable the energy efficiency. Torque sharing function (TSF) has been used in torque control. Torque ripple minimization has been examined on the effects of TSF. Turn on angle, rising angle, falling angle, turn of angle affect the torque ripple. SRM's flux-current-position and torque-current-position data has been obtained by using the finite element method (FEM). SRM's model has been created with the help of genetic - artificial neural networks. Inductance characteristic of SRM has been examined with this model. We have seen the advantages of increasing the number of turns. Genetic - fuzzy logic controller is proposed for the motor's torque drive system. Torque and flux rule bases of Fuzzy Logic has determined with Genetic Algorithms (GA). Three-angle and four-angle simulations have done with the proposed method. As a result, the torque ripple has been reduced by using the proposed four-angle genetic fuzzy optimization technique.