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基于概率的隧道磁阻传感器静态磁滞模型研究

Research of Probability-Based Tunneling Magnetoresistive Sensor Static Hysteresis Model.

作者信息

Li Yutao, Wang Liliang, Yu Hao, Qian Zheng

机构信息

School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

High Voltage Research Institute, China Electric Power Research Institute, Beijing 100192, China.

出版信息

Sensors (Basel). 2021 Nov 18;21(22):7672. doi: 10.3390/s21227672.

DOI:10.3390/s21227672
PMID:34833745
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8625321/
Abstract

Tunneling magnetoresistive (TMR) sensors have broad application prospects because of their high sensitivity and small volume. However, the inherent hysteresis characteristics of TMR affect its applications in high accuracy scenarios. It is essential to build a model to describe the attributes of hysteresis of TMR accurately. Preisach model is one of the popular models to describe the behavior of inherent hysteresis for TMR, whereas it presents low accuracy in high-order hysteresis reversal curves. Furthermore, the traditional Preisach model has strict congruence constraints, and the amount of data seriously affects the accuracy. This paper proposes a hysteresis model from a probability perspective. This model has the same computational complexity as the classic Preisach model while presenting higher accuracy, especially in high-order hysteresis reversal curves. When measuring a small amount of data, the error of this method is significantly reduced compared with the classical Preisach model. Besides, the proposed model's congruence in this paper only needs equal vertical chords.

摘要

隧道磁阻（TMR）传感器因其高灵敏度和小体积而具有广阔的应用前景。然而，TMR固有的磁滞特性影响其在高精度场景中的应用。建立一个准确描述TMR磁滞特性的模型至关重要。Preisach模型是描述TMR固有磁滞行为的常用模型之一，但其在高阶磁滞回线中精度较低。此外，传统的Preisach模型具有严格的一致性约束，数据量严重影响精度。本文从概率角度提出了一种磁滞模型。该模型与经典Preisach模型具有相同的计算复杂度，同时具有更高的精度，尤其是在高阶磁滞回线中。在测量少量数据时，该方法的误差与经典Preisach模型相比显著降低。此外，本文提出的模型的一致性仅需要等垂直线段。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/b0b485dee259/sensors-21-07672-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/a225061c4150/sensors-21-07672-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/08ea544667ab/sensors-21-07672-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/1cab87881960/sensors-21-07672-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/b31b744f3455/sensors-21-07672-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/48dd3df7304f/sensors-21-07672-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/bc2a38460c55/sensors-21-07672-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/1b172c2d0fdf/sensors-21-07672-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/79396373f651/sensors-21-07672-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/7cda591435db/sensors-21-07672-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/0c334dfbe819/sensors-21-07672-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/83cc31dfd0b3/sensors-21-07672-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/e422aadd37cc/sensors-21-07672-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/b0b485dee259/sensors-21-07672-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/a225061c4150/sensors-21-07672-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/08ea544667ab/sensors-21-07672-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/1cab87881960/sensors-21-07672-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/b31b744f3455/sensors-21-07672-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/48dd3df7304f/sensors-21-07672-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/bc2a38460c55/sensors-21-07672-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/1b172c2d0fdf/sensors-21-07672-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/79396373f651/sensors-21-07672-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/7cda591435db/sensors-21-07672-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/0c334dfbe819/sensors-21-07672-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/83cc31dfd0b3/sensors-21-07672-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/e422aadd37cc/sensors-21-07672-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea2/8625321/b0b485dee259/sensors-21-07672-g013.jpg

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本文引用的文献

1
Dynamic Ferromagnetic Hysteresis Modelling Using a Preisach-Recurrent Neural Network Model.使用Preisach递归神经网络模型的动态铁磁滞回线建模
Materials (Basel). 2020 Jun 4;13(11):2561. doi: 10.3390/ma13112561.