An Iterative Learning-Based Modulation Scheme for Torque Control in Switched Reluctance Motors

Abstract

This paper deals with an iterative learning approach for modulating the desired torque profile so as to obtain ripple-free torque in switched reluctance motors. Because of the highly nonlinear relation between torque, current, and rotor position for this motor, it is not possible to obtain a closed-form mathematical expression for current as a function of torque and rotor position. Thus, the current waveforms are conventionally computed by using the linear torque model of the motor, and it is well known that such a scheme results in high torque ripple. In this paper, a novel method is proposed to minimize the ripple. In this new scheme, the current is still computed using the linear torque model, but the value of the torque used for this is not the desired (specified) torque, but rather a modulated-desired torque that is obtained by repeated corrections to the desired torque from iteration to iteration. The conventional rectangular pulse profile is taken as the initial current waveform. The method requires much less a priori knowledge of the magnetic characteristics of the motor. The algorithms have been formulated for both one-phase-on and two-phase-on schemes, for a four-phase switched reluctance motor, in the light of the principles behind iterative learning. Based on the observations from the simulation results of these schemes, a modified scheme has been proposed by incorporating a suitable commutation process, often called torque sharing functions, in order to generate reasonably smooth current waveforms for the ease of tracking by the stator circuit of the motor. The performances of all the proposed schemes have been verified by computer simulation.