Impact of Discretization Techniques on Model Predictive Control of SRMs

Model Predictive Control (MPC) techniques provide high performance and reduced torque ripple in switched reluctance motor (SRM) drives. However, the accuracy of MPC is sensitive to the accuracy of the prediction obtained using discretization of the SRM model. Most of the literature uses only the forward-euler discretization technique in the MPC of SRM drives. However, the performance of other available discretization techniques remains unknown. Therefore, in this paper, three different discretization techniques (Forward euler, Heun,s method and Runge-Kutta (II<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> and IV<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> order methods) are analyzed for the implementation of model predictive control on SRM drives, and their performances are compared. All the techniques have been implemented on an 8/6 4-phase SRM drive using MATLAB/Simulink, and the results are presented.