基于卷积神经网络的永磁同步电机转矩观测器

内嵌式永磁同步电机具有高功率密度、高可靠性和弱磁性等诸多优点，但由于电动机参数具有非线性化特征，导致电磁转矩难以精确估算。该文提出了一种基于卷积神经网络的电磁转矩估算方 法，即转矩观测器。首先，基于所搭建的高保真非线性内嵌式永磁同步电机模型，获得用于神经网络训练的转矩观测器数据；然后，基于所提出的卷积神经网络转矩观测器实现内嵌式永磁同步电机的精确控制；最后，为获取最优的转矩估算误差，在仿真实验阶段对不同参数和结构的卷积神经网络进行了对比和分析。结果表明，该神经网络可以实现电磁转矩的准确估算，所建立的转矩观测器具有良好的性能参数和泛化能力。

Observer of Interior Permanent Magnet Synchronous Machine Torque Based on Convolutional Neural Network

The interior permanent magnet synchronous machines have advantages of high power density, high reliability, field weakening performance etc. However, subject to the nonlinear characteristics of motor parameters, accurate estimation of the electromagnetic motor torque is very difficult. In this paper, a convolutional neural network based electromagnetic torque estimation method, i.e., a torque observer is investigated. Training data of the convolutional neural network are collected from the simulations of a high fidelity nonlinear interior permanent magnet synchronous machine by the means of finite element analysis. Then, a control scheme is adopted to control the interior permanent magnet synchronous machines with the proposed torque observer. In order to reduce the torque estimation error, different parameters and structures of the convolutional neural network are compared. Experimental results show that the proposed convolutional neural network based torque observer can estimate the electromagnetic torque accurately.