Torque ripple suppression in switched reluctance motors using iterative learning control to optimize the torque sharing function

Torque ripple caused by uneven torque transfer during phase commutation limits the low-speed steady-state performance of switched reluctance motors (SRMs). This paper proposes a control strategy that uses iterative learning control (ILC) to optimize the torque sharing function (TSF). Starting from a cosine TSF, the method uses the torque error recurring within each electrical cycle to update the TSF in the commutation region from one cycle to the next. The resulting reference torque distribution progressively approaches the actual electromagnetic torque response, thereby improving torque transfer between phases. An 8/6-pole SRM drive model is developed using ANSYS/Maxwell and MATLAB/Simulink to evaluate the proposed strategy. Compared with conventional TSF control, the proposed strategy reduces the torque ripple rate of the SRMs by 39.69%,38.86%, and 38.29% under low-speed steady-state operating conditions of 100 r/min, 200 r/min, and 400 r/min, respectively, demonstrating its effectiveness in suppressing torque ripple.