Modeling and control of magnetorheological fluid dampers usi

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Modeling and control of magnetorheological fluid dampers using neural networks

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INSTITUTEOFPHYSICSPUBLISHINGSmartMater.Struct.14(2005)111–126

SMARTMATERIALSANDSTRUCTURES

doi:10.1088/0964-1726/14/1/011

Modelingandcontrolof

magnetorheological uiddampersusingneuralnetworks

DHWangandWHLiao1

SmartMaterialsandStructuresLaboratory,DepartmentofAutomationandComputer-AidedEngineering,TheChineseUniversityofHongKong,Shatin,NT,HongKongE-mail:whliao@cuhk.edu.hk

Received27February2003,in nalform3July2004Published7December2004

http:///SMS/14/111

Abstract

Duetotheinherentnonlinearnatureofmagnetorheological(MR) uiddampers,oneofthechallengingaspectsforutilizingthesedevicesto

achievehighsystemperformanceisthedevelopmentofaccuratemodelsandcontrolalgorithmsthatcantakeadvantageoftheiruniquecharacteristics.Inthispaper,thedirectidenti cationandinversedynamicmodelingforMR uiddampersusingfeedforwardandrecurrentneuralnetworksarestudied.Thetraineddirectidenti cationneuralnetworkmodelcanbeusedtopredictthedampingforceoftheMR uiddamperonline,onthebasisofthedynamicresponsesacrosstheMR uiddamperandthecommandvoltage,andtheinversedynamicneuralnetworkmodelcanbeusedtogeneratethecommandvoltageaccordingtothedesireddampingforcethroughsupervisedlearning.ThearchitecturesandthelearningmethodsofthedynamicneuralnetworkmodelsandinverseneuralnetworkmodelsforMR uiddampersarepresented,andsomesimulationresultsarediscussed.Finally,thetrainedneuralnetworkmodelsareappliedtopredictandcontrolthedampingforceoftheMR uiddamper.Moreover,validationmethodsfortheneuralnetworkmodelsdevelopedareproposedandusedtoevaluatetheirperformance.Validationresultswithdifferentdatasetsindicatethattheproposeddirectidenti cationdynamicmodelusingtherecurrentneuralnetworkcanbeusedtopredictthedampingforceaccuratelyandtheinverseidenti cationdynamicmodelusingtherecurrentneuralnetworkcanactasadampercontrollertogeneratethecommandvoltagewhentheMR uiddamperisusedinasemi-activemode.

(Some guresinthisarticleareincolouronlyintheelectronicversion)

1.Introduction

1.1.MR uiddampers

Magnetorheological(MR) uidsaresuspensionsthatexhibitrapid,reversible,andtunabletransitionfromafree- owingstatetoasemi-solidstateupontheapplicationofanexternalmagnetic eld.Thesematerialsdemonstratedramaticchangesintheirrheologicalbehaviorinresponsetoamagnetic eld(CarlsonandWeiss1994).MR uidshaveattracted

1Authortowhomanycorrespondenceshouldbeaddressed.

considerableinterestrecentlybecausetheycanprovideasimpleandrapidresponseinterfacebetweenelectroniccontrolsandmechanicalsystems(Kordonsky1993a,1993b).TheMR uiddampers,whichutilizetheadvantagesofMR uids,aresemi-activecontroldevicesthatarecapableofgeneratingamagnitudeofforcesuf cientforlarge-scaleapplications,whilerequiringonlyabatteryforpower(Dykeetal1996,Spenceretal1997).Additionally,thesedevicesofferhighlyreliableoperationsandtheirperformancesarerelativelyinsensitivetotemperature uctuationsorimpuritiesinthe uid.Inrecentyears,researchintoanddevelopmentof

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0964-1726/05/010111+16$30.00©2005IOPPublishingLtdPrintedintheUK

DHWangandWHLiao

MR uiddampersandtheirapplicationshavebeenattractivetomanyresearchers.Already,a20tonMR uiddamperprototypehasbeendevelopedandtestedinthelaboratory(Spenceretal1998)andapplicationsofMR uiddamperscanbefoundovertherangefromcivilstructuressuchasbuildingsandbridges(Dykeetal1996,Housneretal1997,Spenceretal1998)toautomobiles(Choietal2000)andrailwayvehicles(LiaoandWang2003).

DuetotheinherentnonlinearnatureofMR uiddampers,oneofthechallengingaspectsforachievingahighlevelofperformanceisdevelopmentofaccuratemodelsandcontrolalgorithmsthatcantakeadvantageoftheuniquecharacteristicsofMRdevices.

1.2.ThemodelingchallengeforMR uiddampers

Recently,bothnon-parametricandparametricmodelshavebeenproposedfordescribingthebehaviorofMR uiddampers.Parametricmodelsbasedonmechanicalidealizationshavebeenconsideredbyseveralresearchers(Spenceretal1997,Wereleyetal1998,Lietal2000).Spenceretal(1997)proposedthemodi edBouc–WenmodelfordescribingbehaviorofanMR uiddamper,inwhich14parametersneedtobedeterminedthroughcurve ttingofexperimentaldata,whichisverytime-consuming(LaiandLiao2002).Pangetal(1998)discussedfourmodelsfordescribingthebehaviorofMR uiddampers,namely:(1)theBinghamplasticmodel,(2)thebiviscousmodel,(3)thehystereticbiviscousmodel,and(4)theviscoelastic–plasticmodel;andLietal(2000)testedtheabovemodelsforthefrequencyrangeupto12Hz.TheseparametrizedmodelscanmodelthedynamicsofMR uiddamperswithinalimitedrange.

However,parametricidenti cationmethodsrequireassumptionsasregardsthestructureofthemechanicalmodelthatsimulatesbehavior.Onceamodelisselected,thevaluesofsystemparametersaredeterminedinsuchawaythattheerrorbetweentheexperimentalandthesimulatedresponsesisminimized.Theapproachcouldbedivergentifthestartingassumptionsforthestructureofthemodelare awed,orifproperconstraintsarenotappliedtotheparameters.Unrealisticparameterssuchasnegativemassorstiffnessmaybeobtained.Non-parametricmethodscouldavoidsomepitfallsoftheparametricapproachesformodeling,whicharerobustandapplicabletolinear,nonlinear,andhystereticsystems(EhrgottandMasri1992).FormodelingMR uiddampers,ChangandRoschke(1998)proposedanon-parametricmodelusingneuralnetworks,inwhichafeedforwardneuralnetwork(FNN)isused.TrainingandpredictionofthenetworkrelyoninputandoutputinformationonMR uiddampers.SchurterandRoschke(2000)investigatedthemodelingofMR uiddamperswithanadaptiveneuro-fuzzy …… 此处隐藏：35682字，全部文档内容请下载后查看。喜欢就下载吧 ……