伺服电机转子位置检测的误差分析与补偿

张天恒,童鹏,杨继森

计量学报 ›› 2022, Vol. 43 ›› Issue (10) : 1319-1325.

PDF(50126 KB)
PDF(50126 KB)
计量学报 ›› 2022, Vol. 43 ›› Issue (10) : 1319-1325. DOI: 10.3969/j.issn.1000-1158.2022.10.12
电磁学计量

伺服电机转子位置检测的误差分析与补偿

  • 张天恒,童鹏,杨继森
作者信息 +

Error Analysis and Compensation of Servo Motor Rotor Position Detection

  • ZHANG Tian-heng,TONG Peng,YANG Ji-sen
Author information +
文章历史 +

摘要

针对伺服电机转子位置检测中存在安装不方便、成本高等问题,提出了基于隧道磁阻效应和时栅技术相结合的转子位置检测单元的设计方案。将空间正交的一对TMR传感单元嵌入在电机的前端盖上,实现嵌入式位置精密检测。根据检测单元转子位置解算原理,分析了检测单元的安装误差、电气误差、电磁噪声误差等引起的误差成分。提出了基于超限学习机的误差补偿方法,通过对真实值和测量值样本的训练得到模型最优参数,根据模型参数建立转子位置的误差模型。利用所得到误差模型实现对转子位置的误差补偿。实验结果表明,在2000r/min匀速工况下,补偿前转子位置最大测量误差为4.64°,补偿后转子位置误差为0.315°,精度提升了93.2%,为伺服电机转子位置检测提供了新的方法。

Abstract

Aiming at the problems of low accuracy, inconvenient installation and high cost in current motor rotor position measurement, a design scheme of rotor position sensor based on the combination of tunnel magnetoresistance effect and time grating technology is proposed. On the premise of not destroying the original structure of the motor, a pair of space orthogonal TMR chips are embedded in the front cover of the motor to realize the embedded position precise detection. According to the principle of TMR sensor rotor position calculation, the error components caused by installation error, electrical error and electromagnetic noise error of TMR sensor are analyzed. an error compensation method based on extreme learning machine is proposed. The model optimal parameters are obtained through training the real sample values and measured values. According to the model parameters, Rotor position error model is established, which is used to realize rotor position error compensation.The experimental results show that under the uniform speed of 2000r/min, the maximum measurement error of rotor position before compensation is 4.64° and the rotor position error is 0.315° after compensation, and the accuracy is improved by 93.2%.The embedded rotor position detection method provides a new method for motor rotor position detection.

关键词

计量学 / 电机转子 / 位置检测 / 隧道磁阻 / 嵌入式位置 / 时栅 / 超限学习机

Key words

metrology / motor rotor / position detection / tunnel magnetoresistance / embedded position / time grating / extreme learning machine

引用本文

导出引用
张天恒,童鹏,杨继森. 伺服电机转子位置检测的误差分析与补偿[J]. 计量学报. 2022, 43(10): 1319-1325 https://doi.org/10.3969/j.issn.1000-1158.2022.10.12
ZHANG Tian-heng,TONG Peng,YANG Ji-sen. Error Analysis and Compensation of Servo Motor Rotor Position Detection[J]. Acta Metrologica Sinica. 2022, 43(10): 1319-1325 https://doi.org/10.3969/j.issn.1000-1158.2022.10.12
中图分类号: TB971   

参考文献

[1]刘帅. 森天电机公司竞争战略研究[D]. 长春: 吉林大学, 2015.
[2]朱高林, 肖遥剑, 赵浩, 等. 永磁无刷直流电动机位置传感器精度对脉动转矩抑制效果的影响研究[J]. 计量学报, 2021, 42(4): 432-437.
Zhu G L, Xiao Y J, Zhao H, et al. research on the Position Sensor Precision Effect for Pulsating Torque Suppression of Permanent Magnet Brush-less DC Motor[J]. Acta Metrologica Sinica, 2021, 42(4): 432-437.
[3]朱国斌, 赵浩. 一种便携式角速度传感器[J]. 计量学报, 2020, 41(8): 965-968.
Zhu G B, Zhao H. A Portable Angular Rate Sensor[J]. Acta Metrologica Sinica, 2020, 41(8): 965-968.
[4]陆盛康, 冯浩, 赵浩. 新型旋转角加速度传感器特性标定系统的研究[J]. 计量学报, 2020, 41(5): 578-584.
Lu S K, Feng H, Zhao H. Research on the Characteristic Tuning System of the New Rotating Angular Acceleration Sensor[J]. Acta Metrologica Sinica, 2020, 41(5): 578-584.
[5]张珂, 田跃, 王琦. 基于TMR 的二线制角度传感器设计[J]. 传感器与微系统, 2016, 35(5): 80-83.
Zhang K, Tian Y, Wang Q. Design of two-wire system angle sensor based on TMR[J]. Sensors and Microsystem, 2016, 35(5): 80-83.
[6]张坤, 周浩. 磁电编码器的测量误差分析及倾斜消差滤波[J]. 兵器装备工程学报, 2016, 37 (12): 113-117.
Zhang K, Zhou H. The measurement error analysis of magnetoelectric encoder and the filtering of tilt error cancellation[J]. Journal of Weapon Equipment Engineering, 2016, 37 (12): 113-117.
[7]薛凌云, 刘震天. 基于神经网络误差补偿的磁编码器细分算法[J]. 杭州电子科技大学学报(自然科学版)2016, 36 (2): 52-61.
Xue L Y, Liu Z T. A Magnetic Encoder subdivision algorithm based on neural network error compensation[J]. Journal of Hangzhou University of Electronic Science and Technology (Natural Science Edition) 2016, 36 (2): 52-61.
[8]张天恒, 王培懿, 武亮, 等. 时栅转台自动标定系统的研究 [J]. 计量学报, 2016, 37(1): 6-9.
Zhang T H, Wang P Y, Wu L, et al. Research on automatic calibration system of time grid turntable [J]. Acta metrologica sinica, 2016, 37(1): 6-9.
[9]武亮, 彭东林, 汤其富, 等. 嵌入式时栅位移传感器测量原理与结构优化[J]. 仪器仪表学报, 2016 ,(5): 338-342.
Wu L, Peng D L, Tang Q F, et al. Measurement principle and structure optimization of parasitic time grating displacement sensor[J]. Chinese Journal of Scientific Instrument, 2016, (5): 338-342.
[10]王淑娴, 吴治峄, 彭东林, 等. 具有位置检测功能的新型交流伺服电机研究[J]. 中国电机工程学报, 2018, 38(22): 6692-6701.
Wang S X, Wu Z Y, Peng D L, et al. Research on the Novel AC Servo Motor With Position Detection Function[J]. Chinese Journal of electrical engineering, 2018, 38(22): 6692-6701.
[11]莫会成, 闵琳. 现代高性能永磁交流伺服系统综述—传感装置与技术篇[J]. 电工技术学报, 2015, 30(6): 10-21.
Mo H C, Min L. Overview of Modern High-performance Permanent Magnet AC Servo System—Sensing Device and Technology[J]. Transactions of China electrotechnical society, 2015, 30(6): 10-21.
[12]李帅, 孙立志, 刘兴亚, 等. 永磁同步电机电流谐波抑制策略[J]. 电工技术学报, 2019, 34(6): 87-96.
Li S, Sun L Z, Liu X Y, et al. Current harmonic suppression strategy of permanent magnet synchronous motor [J]. Journal of Electrical Technology, 2019, 34(6): 87-96.
[13]Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1): 489-501.
[14]刘彬, 杨有恒, 刘静, 等. 基于流形正则化的批量分层编码极限学习机[J]. 计量学报, 2021, 42(7): 937-943.
Liu B, Yang Y H, Liu J, et al. Batch Hierarchical Coding Extreme Learning Machine Based on Manifold Regularization[J]. Acta Metrologica Sinica, 2021, 42(7): 937-943.
[15]Liu Y X, Li X S, Zhang X J, et al. Novel calibration algorithm for a three-axis strapdown magnetometer[J]. Sensors, 2014, 14(5): 8485-8504.
[16]董泽, 马宁, 李长青. 基于偏最小二乘和超限学习机结合的电站锅炉NOx排放建模[J]. 东南大学学报(英文版), 2019, 35(2): 179-184.
Dong Z, Ma N, Li C Q. NOx emission model for coal-fired boilers using partial least squares and extreme learning machine[J]. Journal of Southeast University (English Edition), 2019, 35(2): 179-184.

基金

国家自然科学基金(51875069,52175454);重庆市教委项目(KJQN201801108);重庆市基础研究与前沿探索专项(cstc2021jcyj-msxmX0375)

PDF(50126 KB)

Accesses

Citation

Detail

段落导航
相关文章

/