An Online Torque Sharing Function with Torque Reference Self-Adjusting Method for Switched Reluctance Machines

This paper proposes a torque sharing function (TSF) for Switched reluctance motor (SRM) based on the torque closed-loop compensation. Firstly, the online TSF method proposed in this paper takes the negative torque caused by current tailing into account and compensate it in the incoming phase. Secondly, the reference torque of incoming phase and outgoing phase are compensated through torque closed-loop, which enhance the system response ability. Thirdly, six-segment commutation intervals of the traditional TSF methods are divided into twelve segments and the switching states of commutation interval are reassigned, which reduces torque ripple caused by current tracking errors effectively. Simulation of the proposed TSF method and traditional TSF methods have been done on MATLAB. The results prove that the proposed TSF method has lower torque ripple and better current tracking capability compared to the traditional TSF.