Torque Ripple Reduction of a Small Power Switched Reluctance Motor Based on Iterative Learning Control
Li Hongmei Zhang Zhiquan Li Zhongjie (Hefei University of Technology Hefei 230009 China)
Optimizing phase current waveforms of switch reluctance motors (SRM) to reduce torque ripple becomes a hotspot question for foreign and domestic researchers to deduce torque ripples of SRM. Based on linear and nonlinear models of SRM, a novel iterative learning control strategy with torque feedback was presented in this paper. Through iterative learning, the given current value is adjusted by different rotor positions to optimize phase current waveform. The simulation results validate that the presented iterative learning control strategy with torque feedback not only has the merit of high precision and fast convergent speed, but also reduces the torque ripples of SRM.
【CateGory Index】： TM352