一种基于改进模型预测控制算法的永磁同步电机多参数辨识研究

Research on Multi-parameter Identification of PMSM Based on an Improved MPC Algorithm

利用传统的模型预测控制算法确定下一时刻所需要的开关状态时,需要遍历功率变流器的全部开关状态,计算量大,不利于在线应用;同时,精确的参数辨识对于电机控制与运行状态监视具有重要意义。因此,提出一种基于改进模型预测控制算法的多参数辨识方法,根据求出的期望电压矢量角判断所在扇区,从对应扇区所包含的开关状态中进行选择,大大降低了计算量;其次将实时辨识的电机参数应用到预测控制器中,保证了系统的稳定性和参数辨识的收敛性,解决了在线辨识参数鲁棒性差、算法复杂等问题。仿真结果表明:参数辨识能够在较短时间内收敛到真实值附近,并且具有较小的误差,系统具有较好的电流动态特性和较小的纹波电流。

All switching statuses of a power converter should be tested when traditional MPC(Model Predictive Control)algorithm is applied to determine the switching status of next cycle,which is unfavorable to the real-time implementation due to its higher computation load.At the same time,accurate parameter identification is important for motor control and operating status monitoring.In this paper,a new method for multi-parameter identification of permanent magnet synchronous motor(PMSM)based on an improved MPC algorithm was proposed.The sector of the voltage vector was determined by finding the desired voltage vector angle,therefore the selection time of the switch status and the calculation time can be reduced significantly.On the other hand,the application of the real-time identification parameters to the MPC controller guarantees the stability of the system and fast convergence of the parameters identification,and the problems such as the poor robustness of the on-line parameter identification,complex algorithm can be solved.Finally,a series of simulations was carried out to verify the proposed method.The simulation results show that the parameter identification can be converged quickly to the real value by the proposed method,and the system has better current dynamic characteristics with smaller ripple current.