Adaptive iterative learning control of switched reluctance motors for minimizing energy conversion loss and torque ripple

In this paper, an adaptive iterative learning control based on the accurate magnetization characteristics of the SRM is proposed to minimize the torque ripple and electromagnetic energy conversion losses by tuning the energization parameters of commutation angles and duty ratio. The electromagnetic energy conversion and torque in SRM are functions of the flux-linkage, current, and rotor angle. The optimal excitation current profile will result in optimal speed response, co-energy generation, and minimum torque ripple. An automatic characterizing system is developed to identify the SRMs' static magnetization curves accurately and take the nonlinearity of the magnetic circuit into account. The dSPACE DS1104 controller is utilized to setup the drive system for simulation and implementation. Experimental tests of a 4-phase 8/6 pole SRM at different operation conditions are given to demonstrate the effectiveness and performance of the proposed method.