基于BP神经网络的永磁同步电机转矩观测器设计

Design of Torque Observer Based on BP Neural Network for Permanent Magnet Synchronous Motor

针对永磁同步电机参数辨识困难、电磁转矩难以通过数学模型来精确估算从而导致电机控制精度以及驱动系统的整体性能下降的问题,设计了一种基于反向传播(BP)神经网络的电机电磁转矩网络拓扑。通过MATLAB/Simulink将该神经网络封装成转矩观测器,用于精确地计算电机转矩。最后通过试验平台进行试验验证,并与传统转矩的计算方式进行对比分析。结果表明:所设计的转矩观测器具有高精度的转矩输出性能,与传统转矩估算数学模型相比,具有更高的控制精度和准确性。

It is difficult to identify the motor parameters of permanent magnet synchronous motor, and the electromagnetic torque is also difficult to accurately estimate by mathematical model, which leads to the decrease of the motor control precision and the overall performance of the drive system. A motor electromagnetic torque network topology based on back-propagation(BP) neural network is designed. The network is packaged into a torque observer by MATLAB/Simulink for accurate calculation of motor torque. Experimental verification and comparison with the traditional calculation method are carried out by the experimental platform. Experimental results show that the torque observer has high-precision torque output performance and the control precision is higher than that of the traditional torque estimation mathematical model.