Iterative Learning Based Torque Ripple Suppression of Flux-Modulation Double-Stator Machine
Yubin Wang, Yun-lei Gao, Chenchen Zhao, Xianglin Li
Abstract
Due to the double-layer airgap configuration, the even-order harmonic components exist in the phase back electromotive force (EMF) of flux-modulation electrically excitation double-stator synchronous machine (FMEE-DSM), thus resulting in relatively high torque ripples. To promptly and effectively suppress these torque ripples, this article proposes and implements a second-order iterative learning control (ILC) strategy to minimize the main harmonic torque components based on the periodically repetitive characteristic of torque ripples in the FMEE-DSM. Via harmonic analysis on the torque ripples caused by the cogging torque and the nonsinusoidal induced EMFs, the dominative harmonic component, namely, the seventh-order component, can be precisely identified and then involved in the proposed ILC for torque ripple suppression. To verify the feasibility of proposed control strategy, a test bench of the FMEE-DSM control system was built based on dSPACE 1103. Accordingly, both the static and dynamic performances of the FMEE-DSM were investigated under the conditions of no-load and rated load. The theoretical analysis, computer simulation, and hardware experimentation are given to verify the proposed second-order ILC.