Torque Ripple Minimization in Switch Reluctance Motor Drives: an ANFACO Based Control

Abstract

Abstract:In this paper, the torque ripples in the switched reluctance motor (SRM) is constrained by utilizing an Ant Colony Optimization and an Adaptive Neuro Fuzzy Inference System (ANFACO). The control scheme is utilized for the simultaneous control of the speed and current of the SRM which fuses two controlling loops of PI controller. The external loop is governed for speed control and the internal loops is governed for current control despite the ideal decision of the turn on and turn off switching angles. With respect to, for instance, speed, current, inductance and torque, the dynamic behavior of the SRM is examined to control the speed and current which restrict the torque ripple. The gain parameters of the PI controller are ideally made as the dataset by the ant colony optimization (ACO) algorithm. The created dataset is passed to the adaptive neuro fuzzy inference system (ANFIS) for the error prediction. By then, the proposed framework is executed in the MATLAB/Simulink working stage. The execution of the proposed method is affirmed by differentiating and the current systems like ant lion optimization (ALO), non dominating sorting search algorithm (NSGA-II) and combined moth flame optimization and genetic algorithm with recurrent neural network (CMFG-RNN) procedures.