Torque Ripple Suppression Method for Switched Reluctance Motor Based on Current Adaptive Torque Sharing Function

Considering the poor torque ripple suppression performance resulting from inefficient torque allocation in conventional offline torque sharing functions (TSFs), this paper proposes an online adaptive TSF based on deadbeat predictive control (DPC). First, the proposed method accounts for the current tracking capability by dynamically distributing the optimal current reference profiles for the incoming and outgoing phases. The rate of change of current (RCC) in the adjacent phases is also considered during the formulation of the TSF expression. Second, to accommodate various operating conditions, a control angle self-adjustment algorithm is proposed. The optimal turn-on angle is determined by minimizing torque ripple through an iterative calculation method. The optimal turn-off angle is identified by comparing the estimated value with the maximum flux linkage at the (k+1) control period. Compared to the optimal angle calculation strategies in the existing literature, the proposed method is not only computationally efficient but also mitigates the angle oscillation phenomenon during the iterative calculation process. Finally, simulation and experimental validation of the proposed method are conducted on a three-phase 12/8 switched reluctance motor (SRM). The results demonstrate that the proposed method offers superior torque ripple suppression and higher torque-per-ampere ratios across different operating conditions compared to the traditional torque control method.