Modelling method for nonlinear output force of electromagnetic actuator

被引:0
|
作者
Liu W. [1 ,2 ]
Gao Y. [1 ,2 ]
机构
[1] Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing
[2] University of Chinese Academy of Sciences, Beijing
关键词
Electromagnetic actuator; Experimental modelling; Gaussian process regression; Nonlinear; Polynomial regression;
D O I
10.15938/j.emc.2020.09.013
中图分类号
学科分类号
摘要
A modelling method combining experimental test and regression analysis is proposed for nonlinear output force of electromagnetic actuator with complex magnetic field distribution. The electromagnetic force at several different spatial points in the working area of magnetic field was measured through the calibration experiment. Based on the calibration experimental data, the electromagnetic force model was established by polynomial regression method and Gaussian process regression method. Based on the polynomial regression method, the polynomial parameter model of electromagnetic force was established, and the coefficient of the polynomial model was calculated by the least square method. Based on the Gaussian process regression method, the conditional probability model of electromagnetic force was established, and the selection of covariance function and the optimization algorithm of super parameters were analyzed. Taking an electromagnetic actuator used in an active vibration isolation device for space microgravity as an example, the prediction accuracy and calculation efficiency of the polynomial model of electromagnetic force and the Gaussian process regression model of electromagnetic force were compared. The calculation time of the polynomial model of electromagnetic force in one prediction is less than 1 ns, and the prediction accuracy of electromagnetic force in the main direction is better than 12.5‰. The calculation time of the Gaussian process regression model of electromagnetic force in one prediction is about 24 ms, and the prediction accuracy of electromagnetic force in the main direction is better than 8‰. © 2020, Harbin University of Science and Technology Publication. All right reserved.
引用
收藏
页码:115 / 125
页数:10
相关论文
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