转矩分配策略在SRM中的应用及其优化

Research and Optimizing of Switched Reluctance Motor Based on Torque Sharing Strategy

转矩脉动较大是开关磁阻电机(SRM)的主要缺点。为了减小转矩脉动,提高电机性能,将转矩分配策略应用到电机中。由于开关磁阻电机的非线性,在转矩分配策略中,传统PID控制器的效果受到了限制。考虑到神经网络对非线性系统的自学习和自适应能力,提出采用RBF神经网络PID控制器进行优化。仿真结果表明,转矩分配策略有效抑制了转矩脉动,优化后的转矩分配策略获得了更好的性能。

High torque ripple is the main disadvantage of switched reluctance motor. So, torque sharing strategy is used to minimize the ripple. Because of the nonlinear property of the motor, in torque sharing strategy control system, the result of using normal PID controller is effective but not very good. Artificial neural network is good at calculating the nonlinear system and it has good self-adaption ability, so RBFNN is used to optimize the normal PID controller. Simulation results show that torque sharing strategy works well and the RBFNN PID controller works better than the normal one.