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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 |
College of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
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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.
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Received: 20 June 2019
Published: 24 September 2021
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