Hybrid intelligent controller based torque ripple minimization in switched reluctance motor drive

Abstract

An inherent torque ripple characterizes switched reluctance technology from conventional technology. The ultimate aim of this paper is to reduce the torque ripple of the switched reluctance motor drive using genetic neural network controller based direct torque scheme. In the proposed controller network appropriate bits of data are chosen for training and testing. The proper selection of the learning rate and momentum will help in weight adjustment. Here the error is reduced which proves that the selection of voltage vectors from the vector table is precise and its results in better torque response over a wide range of speed. The simulation results reveal that the torque ripples vary between 3.25% to 1.7% for the variation in load torque and the drive speed. The experimental results for the proposed controller reveal that the torque ripple varies between 3.7% to 2.1%. Both the simulation and hardware results illustrate the efficiency of the controller.