基于电流迭代优化的SRM总转矩TSF闭环控制

Closed-loop Control of Total Torque TSF for SRM Based on Current Iteration Optimization

开关磁阻电机(SRM)存在低速转矩脉动大的问题,这制约了它在电动汽车领域的推广和应用。以减小转矩脉动为目的,在传统转矩分配控制基础上引入总转矩负反馈,采用迭代学习控制(ILC)算法优化各相参考电流曲线,提出了基于电流迭代优化的SRM转矩分配控制策略;其中优化后的参考电流采用分区细化的滞环控制器跟踪控制。仿真研究结果验证了提出的控制策略可以有效减小电机的转矩脉动。

Switched reluctance motor （SRM） exists a problem of the high torque ripple at low speed, which restricts its popularization and application in the field of electric vehicles. Based on the open-loop control structure of traditional torque-sharing function（TSF）, a novel TSF control scheme combined with current iterative learning control （ILC） for torque-ripple minimization of SRM was proposed through introducing the negative feedback of total torque and adopting the algorithm of iterative learning control to optimize reference current curve of each phase. Among them, the optimized reference current was tracked and controlled by a hysteretic controller with partition refinement. Simulation results verify that the control strategy proposed can effectively reduce the torque ripple of the motor.