Minimization the torque ripple of flux-switching permanent magnet motor based on iterative learning control

With the advent of flux-switching permanent magnet (FSPM) motors, researches on both design and control of such motors have made great achievements, which has proved that the type of motors have great application prospects. However, due to the high cogging torque caused by doubly-salient structure and the high air gap flux density, such motors are limited on the occasions of servo system or traction application. To solve the problem, a new method of cogging torque compensation for an external-rotor 12/22 pole FSPM motor is proposed in this paper. Considering the advantages of Iterative Learning Control (ILC) in motion control, it is introduced into the Direct Torque Control (DTC) system of FSPM motor in this paper, compensating for the given value of torque. The simulation results prove that this method is effective and does not influence the performance of dynamic performances.