Iterative learning based torque controller for switched reluctance motors

Torque ripples in switched reluctance motor (SRM) prevent it from being used in high performance applications. The highly non-linear nature of SRM magnetization characteristics, which is difficult to model, is the root cause of the problem. A non-linear controller based on accurate model of its magnetic characteristics is not of much help towards general promotion of SRM. We have proposed a torque controller for SRM using iterative learning, which does not require a model of the SRM. An indirect torque control scheme is adopted for its well known advantages. The cascaded torque controller consists of three subunits: 1) torque sharing function (TSF), 2) torque to current conversion, and 3) current controller. Iterative learning has been used in both torques to current conversion as well as current controller design. The proposed torque controller has been experimentally verified for an 8/6 pole SRM.