[关键词]
[摘要]
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。
[Key word]
[Abstract]
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.
[中图分类号]
[基金项目]
国家自然科学基金项目(51207012);陕西省工业科技攻关项目(2016GY-069);陕西省微特电机及驱动技术重点实验室开放基金项目(2013SSJ2002);中央高校基本科研业务费专项资金资助项目(300102228201);陕西省自然科学基金项目(2020JQ-385)