基于PSO算法的GMM改进J-A磁滞模型的参数辨识与验证

滕峰成; 王珊珊; 杨雪璠; 吕登岩

doi:10.3969/j.issn.1000-1158.2021.09.12

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计量学报 ›› 2021, Vol. 42 ›› Issue (9) : 1193-1199. DOI: 10.3969/j.issn.1000-1158.2021.09.12

电磁学计量

基于PSO算法的GMM改进J-A磁滞模型的参数辨识与验证

滕峰成,王珊珊,杨雪璠,吕登岩

作者信息 +

Parameter Identification and Verification of Improved J-A Hysteresis Model of GMM Based on PSO Algorithm

TENG Feng-cheng,WANG Shan-shan,YANG Xue-fan,L Deng-yan

Author information +

文章历史 +

摘要

为了解决现有的GMM-FBG电流传感器的磁滞非线性问题,基于经典的J-A磁滞模型提出了一种改进的适用于低频(<120Hz)条件下的J-A模型。采用粒子群(PSO)算法对改进后的J-A模型进行了分段参数辨识与优化,提高了模型的预测精度。搭建了相应的GMM-FBG交流电流传感系统实验平台,运用所提出的改进的J-A模型对GMM-FBG电流传感器进行了磁滞建模和实验验证。实验及仿真结果证实该模型具有良好的预测性,模型的预测误差在2.5%以内,传感系统的电流测量灵敏度达到0.067nm/A。

Abstract

In order to solve the hysteresis nonlinearity of GMM-FBG current sensor, an improved J-A model for low frequency (<120Hz) based on the classical J-A hysteresis model was proposed. The improved J-A model was identified and optimized by using particle swarm optimization (PSO) algorithm segmentedly, which improved the prediction accuracy of the model. The corresponding experimental platform of GMM-FBG AC current sensing system was built. The improved J-A model was used to conduct hysteresis modeling and experiment verification of GMM-FBG current sensor. The experimental and simulation results confirm that the model has good predictability. The prediction error of the model is less than 2.5%, and the current measurement sensitivity of the sensing system reaches 0.067nm/A.

导出引用

滕峰成,王珊珊,杨雪璠,吕登岩. 基于PSO算法的GMM改进J-A磁滞模型的参数辨识与验证[J]. 计量学报. 2021, 42(9): 1193-1199 https://doi.org/10.3969/j.issn.1000-1158.2021.09.12

TENG Feng-cheng,WANG Shan-shan,YANG Xue-fan,L Deng-yan. Parameter Identification and Verification of Improved J-A Hysteresis Model of GMM Based on PSO Algorithm[J]. Acta Metrologica Sinica. 2021, 42(9): 1193-1199 https://doi.org/10.3969/j.issn.1000-1158.2021.09.12

中图分类号： TB971

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