Adaptive Torque Ripple Suppression Strategy for Switched Reluctance Machine Based on Fuzzy Iterative Learning Control

In this paper, a current control strategy based on fuzzy-iterative learning is proposed to suppress the torque ripple of the switched reluctance machine. To optimize the current following performance, the dead-beat predictive current control is adopted. On this basis, for the problem of excessive deviation of the reference current calculated by the linear inductance model, the reference current of each phase is adaptively adjusted in real time by a fuzzy iterative learning strategy to compensate the error into the reference current. The results show that the proposed control method can effectively suppress the torque ripple.