针对表贴式永磁同步电机(PMSM)模型预测转矩控制(MPTC)计算复杂的问题，训练卷积神经网络(CNN)以替代MPTC实现电压矢量的选择。仿真结果表明基于CNN的MPTC稳态性能与传统MPTC基本相当，但由于稳态和动态数据不平衡，使得基于CNN的MPTC动态下磁链脉动较大。因此，提出根据系统状态将基于CNN的MPTC与直接转矩控制(DTC)自适应切换使用的策略。仿真结果表明，该策略在保证稳态控制性能的同时可以有效抑制动态下磁链脉动。

Aiming at the problems of large calculation of model predictive torque control (MPTC) for surface-mounted permanent magnet synchronous motor (PMSM), the convolutional neural network (CNN) is used to replace the MPTC for selecting voltage vector among candidate voltage vectors. Simulation results show that the steady-state control performances of the MPTC based on CNN are basically equivalent to those of the conventional MPTC. But the imbalance between the training data at steady and dynamic states deteriorates the control of the MPTC based on CNN at dynamic state and results in large stator flux ripple. Therefore, the adaptive switching strategy between the MPTC based on CNN and the direct torque control (DTC) is proposed, with the state of the system as the switching criterion. Simulation results show that the adaptive switching strategy can achieve high steady-state performance and suppress stator flux ripple at the dynamic state.

陕西省自然科学基金项目(2021JM-163)