磁链与电流自适应补偿的开关磁阻电机TSF的抑制转矩脉动控制

Torque Ripple Suppression of Switched Reluctance Motor Using TSF Based on Flux and Current Self-adaptive Compensation

转矩分配策略是目前抑制开关磁阻电机(SRM)转矩脉动的主要控制策略之一,通过对各相电流的控制,保证各相瞬时转矩之和为恒定值,以达到减小转矩脉动的目的。由于传统转矩分配控制采用SRM的线性模型,所设计的参考电流与理想的分配电流存在偏差,因而难以达到理想的控制效果。该文提出了一种磁链与电流自适应补偿的TSF优化方案,在传统转矩分配控制基础上,引入有限差分扩展卡尔曼滤波(FDEKF)预估磁链,间接补偿电流和柔性神经网络(FNN)自适应PID直接补偿电流,优化得到恒转矩下的较理想的参考电流波形,间接达到减小转矩脉动的目的。仿真结果验证了该文提出的控制策略可以有效抑制转矩脉动,控制效果明显改善。

The torque-sharing strategy is one of the main strategies in torque ripple suppression of switched reluctance motor （SRM） at present, which ensures that the sum of each phase instantaneous torque at a constant value and reduces the torque ripple by controlling each phase current. Since the deviation between the reference current and the ideal distribution current that caused by the linear model of SRM in conventional torque distribution control, the performance of control is difficult to achieve the ideal state. In order to indirectly reducing the torque ripple, the optimized algorithm of TSF based on flux and current self-adaptive compensation was proposed in this paper. To optimize the ideal phase current waveforms under constant torque on the basis of the traditional torque TSF control, the finite-difference extended Kalman filter （FDEKF） was introduced to estimate the flux and the flexible neural network self-adaptive PID was employed to indirectly and directly compensate proposed control strategy can effectively suppress current, respectively. Simulation results verify that the torque ripple of the motor and has a good control effect.