为实现永磁同步电机（permanent magnet synchronous motor, PMSM）的无传感器控制，提出了一种基于误差修正的长短时记忆神经网络（long short-term memory, LSTM）转速观测模型。该模型结合了LSTM神经网络与电机数学模型，将电机转速的实际值与数学模型计算值之间的差值（即数学模型计算的误差）作为神经网络的输出量进行训练，实现了电机转速的实时观测。将该观测模型应用于PMSM矢量控制系统，对电机负载转矩和给定转速突变等不同运行工况下进行了仿真分析，并与直接将电机转速作为输出量训练的神经网络观测模型进行了比较。结果表明，基于转速误差训练的神经网络训练负担更小，转速观测更准确。此外，结合电机系统特性改进神经网络模型，进一步优化转速观测的精度。仿真验证表明，改进后模型的转速观测误差更小，转速观测精度达99.084%，电机系统运行稳定，验证了该方法的有效性与优越性。

In order to realize the sensorless control of PMSM (permanent magnet synchronous motor), an error-corrected LSTM(long short-term memory) speed observation model was proposed. The model combined the LSTM neural network with the motor mathematical model. The difference between the actual value of the motor speed and the calculated value of the mathematical model(the error calculated by the mathematical model) was trained as the output of the neural network, and the real-time observation of the motor speed was realized. The model was applied to the PMSM vector control system to simulate the load torque and given speed change of the motor under different operating conditions, and was compared with the neural network observation model which directly trained the motor speed as output. The results show that the training burden of neural network based on speed error training is less and the speed observation is more accurate. In addition, combined with the characteristics of the motor system, the neural network model was improved to optimize the accuracy of speed observation further. The simulation results show that the speed observation error of the improved model is smaller, the speed observation accuracy is 99.084%, and the motor system runs stably, which verifies the effectiveness and superiority of the method.