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文章摘要
基于概率的TMR传感器磁滞建模方法的研究
Research on probability-based hysteresis modeling method of TMR sensor
Received:April 18, 2022  Revised:April 25, 2022
DOI:10.19753/j.issn1001-1390.2025.03.008
中文关键词: TMR传感器  磁滞  概率
英文关键词: TMR sensor, hysteresis, probability
基金项目:国家自然科学基金资助项目( 61573046)
Author NameAffiliationE-mail
LI Yutao* School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China liyutaobd@buaa.edu.cn 
WANG Liliang School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China wangliliang@buaa.edu.cn 
YU Hao China Electric Power Research Institute, Beijing 100192, China yuhao@epri.sgcc.com.cn 
QIAN Zheng School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China qianzheng@buaa.edu.cn 
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中文摘要:
      磁滞现象是影响TMR(tunnel magneto resistance)传感器测量精度的主要因素之一,一般通过磁滞分析对磁滞进行补偿降低其影响。常用的磁滞分析通常基于Presach模型,存在建模复杂、计算速度慢等缺点,在实际应用中不能充分提升TMR测量精度。针对TMR磁滞特性建模问题,文中提出了一种基于磁滞算子概率估计的建模方法。对磁滞算子转换阈值的概率分布进行估计,构建TMR传感器静态磁滞模型;估计不同频率磁场下磁滞算子转换速度的概率分布,与静态磁滞模型结合,构建TMR传感器动态磁滞模型。文中采用了两个典型TMR传感器对所提出的磁滞模型进行验证,误差最大值减小13.3%,方差减小52.1%。结果表明文中方法可有效提升TMR传感器测量精度,且计算简单,具有较好的适用性。
英文摘要:
      Hysteresis is one of the main factors affecting the measurement accuracy of TMR (tunnel magneto resistance) sensors. Generally, hysteresis is compensated by hysteresis analysis to reduce its influence. The commonly used hysteresis analyses are usually based on the Presach model, which has the disadvantages of complex modeling and slow calculation speed and cannot fully improve the measurement accuracy of TMR in practical application. Aiming at the modeling problem of TMR hysteresis characteristics, a modeling method based on hysteresis operator probability estimation is proposed in this paper. The probability distribution of the conversion threshold of the hysteresis operator is estimated, and the static hysteresis model of the TMR sensor is constructed. The probability distribution of the conversion speed of the hysteresis operator under different frequency magnetic fields is estimated and combined with the static hysteresis model, the dynamic hysteresis model of TMR sensor is constructed. Two typical TMR sensors are used to verify the proposed hysteresis model. The maximum error is reduced by 13.3%, and the variance is reduced by 52.1%. The results show that the proposed method can effectively improve the measurement accuracy of the TMR sensor, and the calculation is simple and has good applicability.
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