资源描述：
《基于杂草算法的超磁致伸缩作动器耦合模型识别研究》由会员上传分享，免费在线阅读，更多相关内容在工程资料-天天文库。
1、基于杂草算法的超磁致伸缩作动器耦合模型识别研究杨理华1，吴海平1，刘树勇2，李海峰1（1.海军潜艇学院动力系，山东青岛266199；2.海军工程大学动力工程学院，湖北武汉430033）摘要：考虑磁滞损耗、动态应力等因素的超磁致伸缩作动器磁滞模型，可有效揭示电-磁-机-热多场耦合效应，准确识别其非线性模型往往存在较大困难。智能杂草算法(IWO)具有激烈的竞争机制和较强的搜索能力，可用于解决作动器多目标物理参数辨识问题。传统算法的种子数量以线性方式产生，且分布方差与适应度缺乏联系，这将极大地影响算法收敛速度和模型识别精度
2、。为此，本文提出一种非线性繁殖和分布的混合改进杂草算法，并将其应用于超磁致伸缩作动器模型识别。实验表明：改进算法具有较强的噪声抑制能力，能精确辨识含有噪声扰动的作动器磁滞非线性模型物理参数，模型预测值和实验数据误差较小，所识别参数可使磁滞非线性模型较为全面地描述作动器多场耦合机理和动态特性。关键词：超磁致伸缩作动器；磁滞非线性；模型识别；改进杂草算法中图分类号：O328文献标志码：A 文章编号：ResearchesonCouplingModelIdentificationofGiantMagnetostricti
3、veActuatorUsingInvasiveWeedAlgorithmYANGLihua1，WUHaiping1，LIUShuyong2，LiHaifeng1(1.PowerControlDepartment,NavySubmarineAcademy,Qingdao266042,Shandong,China;2.CollegeofPowerEngineering,NavalUniversityofEngineering,Wuhan430033,Hubei,China)Abstract:Thehysteresismo
4、delofgiantmagnetostrictiveactuator,consideringthehysteresislossanddynamicstress,cancomprehensivelyrevealtheelectro,magnetic,mechanicalandthermalmulti-fieldcouplingeffect.However,itisoftendifficulttoaccuratelyidentifythenonlinearmodelbytheexperiment.Theintellige
5、ntinvasiveweedalgorithm(IWO),withafiercecompetitionmechanismandastrongsearchability,whichisverysuitableforsolvingtheproblemofmultiobjectivephysicalparametersidentification.Nevertheless,thenumberofseedsislinearlygeneratedintraditionalIWOandthedistributionvarianc
6、eislackofadaptabilityaswell,whichwillgreatlyaffectthealgorithmconvergencespeedandmodelrecognitionaccuracy.Therefore,animprovedalgorithmwithnonlinearpropagationanddistributionisproposedandappliedtothemodelparametersidentificationofgiantmagnetostrictiveactuatorin
7、thispaper.Andtheexperimentalsoexhibitsthattheimprovedalgorithmhasstrongnoisesuppressionability,whichcanaccuratelyidentifythephysicalparametersofthehysteresisnonlinearmodelwiththenoisesignal,andtheerrorsbetweenmodelpredictionsandexperimentaldatasaremuchsmaller,t
8、hustheidentifiedparameterscanmakethehysteresisnonlinearmodelcomprehensivelydescribetheactuatormulti-fieldcouplingmechanismanddynamiccharacteristics.Keywords:giantmagnetostri
