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.
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.
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. Acta Metrologica Sinica, 2021, 42(9): 1193-1199.
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2021, Vol. 42 
