辅助线法预测Preisach类一阶回转曲线
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TM27

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(国家自然科学基金资助项目(11373049, 11190013)


Preisach First Order Reversal Curve Predicted by Auxiliary Line Method
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    摘要:

    为了实现对磁致伸缩和压电材料迟滞特性的描述,建立高精度前馈补偿系统,对Preisach模型一阶回转曲线的预测方法进行了研究。首先,介绍了一阶回转曲线及经典插法预测一阶回转曲线的基本原理,着重指出线性经典插值法对Preisach模型一阶回转曲线的预测存在不足;其次,在此基础上,利用非线性变换的思想,提出了辅助线法预测一阶回转曲线;最后,实验比较了超磁致伸缩材料(giant magnetostrictive material,简称GMM)与压电陶瓷(piezoelectric ceramic transducer,简称PZT)两种迟滞情况下,辅助线法和经典插值法对迟滞一阶回转曲线的预测结果。实验结果表明:在GMM迟滞下,对任意一阶回转曲线的预测数据,辅助线法的均方根误差(root mean square error,简称RMSE)最大减少为经典插值法的14.22%;对所有预测数据,辅助线法的RMSE减少为经典插值法的29.42%;在PZT迟滞下,对任意一阶回转曲线的预测数据,辅助线法的RMSE最大减少为经典插值法的18.18%;对所有预测数据,辅助线法的RMSE减少为经典插值法的41.07%。辅助线法对一阶回转曲线的预测精度整体高于经典插值法,且迟滞效应的非线性误差越高,预测精度较经典插值法越优异。

    Abstract:

    In order to describe the hysteresis of magnetostrictive and piezoelectric materials and establisha high-precision feedforward compensation system, the prediction method of Preisach model’s first order reversal curve is studied.Firstly, the fundamental theorem of the first order reversal curve and the classical interpolation method is introduced. The deficiency of the linear classical interpolation method in the prediction of the first order reversal curve is pointed out.Then, based on the idea of nonlinear transformation, an auxiliary line method is proposed to predict the first order reversal curve.Finally, experiments are carried out to predict the first order hysteresis curves with auxiliary line method and classical interpolation method in giant magnetostrictive material (GMM) and piezoelectric ceramic transducer(PZT).In the case of GMM hysteresis, for any first order reversal curve, the root mean square error (RMSE) of the auxiliary line method is reduced to 14.22% of the RMSE of the classical interpolation methodat most. For all prediction data, the RMSE of the auxiliary line method is reduced to 29.42% of the RMSE of classical interpolation method.In the case of PZT hysteresis, for any first order reversal curve, the RMSE of the auxiliary line method is reduced to 18.18% of the RMSE of the classical interpolation method at most. For all prediction data, the RMSE of the auxiliary line method is reduced to 41.07% of the RMSE of classical interpolation method.The prediction accuracy of the auxiliary line methodis higher than the classical interpolation method. And the bigger the nonlinear error of the hysteresis is, the more the accurate of the auxiliary line method will be compared with the classical interpolation method.

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  • 在线发布日期: 2020-05-07
  • 出版日期: 2020-04-30
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