基于卷积神经网络和直接转矩控制的永磁同步电机模型预测转矩控制策略

模型预测转矩控制(MPTC)需要遍历所有备选电压矢量进行预测计算,从中选择最优电压矢量,控制性能良好,但算法计算量大和实时性差。采集MPTC的运行数据离线训练卷积神经网络(CNN),将训练好的CNN代替MPTC进行电压矢量选择。为了解决CNN失控问题,提出了基于CNN控制和直接转矩控制(DTC)的MPTC策略。仿真结果表明,该控制策略可有效解决CNN控制的失控问题,控制效果与MPTC基本相当,转矩和磁链脉动明显低于DTC。

Model Predictive Torque Control Strategy of PMSM Based on Convolutional Neural Network and Direct Torque Control

Model predictive torque control (MPTC) selects the optimal voltage vector by traversing all candidate voltage vectors, which results in large calculation and poor real time performance. In order to solve these problems, the data of MPTC are collected to train a convolutional neural network (CNN) and the CNN is then used to replace MPTC for the optimal voltage vector selection. In order to solve the out of control problem in using CNN, the MPTC based on direct torque control (DTC) and CNN is proposed. Voltage vectors selected by CNN and DTC are used as candidate voltage vectors of MPTC. Simulation results show that the proposed strategy can effectively solve the out of control problem of CNN, its control effect is basically equivalent to MPTC, while the torque and stator flux ripples of the proposed strategy are significantly lower than those of DTC.