为实现永磁同步电机(PMSM)磁链与转矩的定量控制，基于磁链与转矩变化量简化模型，提出永磁同步电机磁链与转矩无差拍(DB)控制策略。实现DB控制需要转矩角信息，PMSM转矩角可通过查表或基于定子磁链d、q轴分量求解，但前者需要大量储存空间，后者需要转子位置实时信息和旋转坐标变换。构建和训练反向传播（BP）神经网络来输出转矩角，并采用BP神经网络替代DB控制实现理想电压矢量角度的预测，建立基于转矩角和理想电压矢量角度预测双BP神经网络的PMSM磁链与转矩DB控制系统。仿真结果表明，BP神经网络可用于预测转矩角和理想电压矢量角度，双BP神经网络的PMSM磁链与转矩DB控制系统运行良好。

In order to realize the quantitative control of the flux and torque in permanent magnet synchronous motor (PMSM), based on the simplified models of flux and torque variation, the deadbeat (DB) control for PMSM is proposed. The torque angle is important to DB control. It can be obtained through look\|up table or the d and q components of stator flux. However, the former needs lots of memory space and the latter needs rotor flux position information. To solve these problems, the back propagation (BP) neural network is used to predict torque angle. Another BP neural network is used to predict the angle of ideal voltage vector to replace conventional calculation. Thus, the DB control of stator flux and torque for PMSM with dual BP neural networks is established. Simulation results show that the BP neural networks can be used to output torque angle and the angle of ideal voltage vector and the motor system works properly.

陕西省自然科学基金项目(2021JM-163);陕西省重点研发计划项目(2019ZDLGY15-06,2020GY-164)