Control of Torque Ripple and Rotor Position for SRM (8/6-4 Phases) Using an Optimization-Based Model Predictive Torque Control

Abstract

Most electric cars and wind turbines employ switched reluctance motors (SRM), but it has some disadvantages, namely high torque ripple because of its power supply mode and multiphase communication. Model predictive torque control (MPTC) with sailfish optimization (SFO) method is proposed to reduce torque ripple of SRM using torque sharing function (TSF). To develop an efficient torque ripple algorithm, the flux-linkage characteristic curves are first acquired at protected rotor trial and create an accurate SRM model. It predicts future operation for drive system in SRM architecture. Second, the SFO algorithm is employed to enhance TSF parameters also to minimize the torque value of SRM. Then, the TSF-based MPTC method is developed to avoid problem like conversion of frequency produced by controller. Finally, atom search optimization (ASO) is used to adjust sensor for correct rotor position of the SRM. To verify the performance of the proposed method, MPTC-SFO is compared with direct instantaneous torque control (DITC) method. Proposed MPTC-SFO method attained more efficient result of 12.79% reduced torque ripple than DITC.