基于模型融合方法的IPMSM转矩预测  

Torque prediction of IPMSM based on model fusion method

作　　者：杜帅祥 韦寿祺[1] 梁嘉宁[2] 孙天夫 王旭 DU Shuaixiang;WEI Shouqi;LIANG Jianing;SUN Tianfu;WANG Xu(School of Mechanical and Electrical Engineering,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]桂林电子科技大学机电工程学院,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：《桂林电子科技大学学报》2022年第6期449-455,共7页Journal of Guilin University of Electronic Technology

基　　金：广东省重点领域研发计划(2020B090925002,2019B090917001);深圳市科技计划(JCYJ20200109115414354)。

摘　　要：在机器人等高端电机控制系统中,由于内嵌式永磁同步电机(IPMSM)运行时,受电机参数辨识困难、谐波干扰等因素影响,电机电磁转矩具有强非线性,很难通过传统数学模型来精确计算,而增加转矩传感器会提高系统成本。为了实现无转矩传感器转矩高精度预测,提出了一种基于模型融合方法的IPMSM转矩预测模型,以简单的线性转矩数学模型融合数据驱动的神经网络算法,可有效减少神经网络模型复杂度,同时提高转矩估算精度。用BP和RBF两种常见网络进行建模仿真和实验验证,证明了该模型可实现转矩实时在线预测,且具有良好的动态稳定性能。In the high-end motor control systems such as robots,due to the difficulty in parameter identification and harmonic interference of the motor during the operation of the internal permanent magnet synchronous motor(IPMSM),it is difficult to accurately calculate the electromagnetic torque of the motor through the traditional mathematical model,which has strong nonlinearity.The increase of torque sensors will increase the system cost.In order to realize high-precision torque prediction of torque-sensorless condition,a torque prediction model of IPMSM based on model fusion method is proposed.A simple linear torque mathematical model is used to fuse data-driven neural network algorithm,which effectively reduces the complexity of neural network model and improves the accuracy of torque estimation.The modeling simulation and experimental verification are carried out by using two common networks:BP and RBF,which proves that the algorithm realizes real-time online torque prediction and has good dynamic stability.

关 键 词：内嵌式永磁同步电机 模型融合 非线性模型 转矩在线估算 无转矩传感器转矩控制 

分 类 号：TM341[电气工程—电机]