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

作　　者：耿建平[1] 闫俞佰 熊光阳 张奎庆 潘家栋 GENG Jianping;YAN Yubai;XIONG Guangyang;ZHANG Kuiqing;PAN Jiadong(School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,China;Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China)

机构地区：[1]桂林电子科技大学电子工程与自动化学院,广西桂林541004 [2]中国科学院深圳先进技术研究院,广东深圳518055

出　　处：《电机与控制应用》2020年第1期78-83,共6页Electric machines & control application

摘　　要：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.

关 键 词：永磁同步电机 转矩观测器 参数辨识 控制精度 系统性能 反向传播神经网络 

分 类 号：TM351[电气工程—电机]