An Optimal Torque Controller Based on Iterative Learning Control for Switched Reluctance Motors for Electric Vehicles

The switched reluctance motor (SRM) is a strong candidate for electric/hybrid vehicle applications primarily because of its low-cost construction and constant power profile, but the torque ripple limited its application. This paper presents an optimal torque controller that is composed of an optimal torque sharing function (TSF) and a current controller. The TSF can optimize the profiles of torque and current to minimize the torque ripple with variable loads. To implement the TSF, a current controller based on iterative learning control (ILC) is presented which considers the mutual inductance with simultaneous two-phase excitation. The results of experiment prove that this optimal torque controller could minimize the torque ripple effectively.