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1、Proceedingsofthe5*WorldCongressonIntelligentControlandAutomation,June15-19,2004,Hangzhou,P.R.ChinaRobustFaultDetectionUsingIterativeLearningObserverforNonlinearSystemsMaLilingWangJunzhengandWangShoukunDepartmentofAutomaticControl,SchoolofInformationScience&Tec
2、hnologyBeijingInstituteofTechnologyBeijing,100081,P.R.Chinamaliling@bit.edu.cnAbstract-Arobustfaultdetectionschemeforaclassofnonlinearsystemswithmodelinguncertaintyandinaccessible11.PROBLEMSTATEMENTANDPRELIMINARIESstateswaspresented.Onlytheinputsandoutputsofth
3、esystemConsideranonlinearsystemmodelgivenbelowcanbemeasured.Anonlineariterativelearningobserverwasutilizedtoproducetheresidualthatwasrobusttouncertainty.Thestabilityofthefaultdetectionschemeundercertaini=Ax+@(x,u)+d(t)assumptionswasanalyzed.Anexampledemonstrat
4、esthey=cxefficiencyoftheproposedfaultdetectionstrategy.wherex(t)ER"istheimmeasurablestatevector,u(t)istheIndexTerms-faultdetection,observer,robustness,iterativemeasurableinputvector,andy(t)isthemeasurableoutputlearning,nonlinearsystemvector.@(x,u)andd(t)repres
5、entaLipschitznonlinearI.INTRODUCTIONfunctionandthemodelinguncertainty,respectively.AandCareconstantmatrices.d(r)denotestheeffectofthenon-faultfactorsonthesystem,suchasmodelingerrors,extemalThegrowingneedforfaultdetection(FD)incomplexdisturbancesanduncertaibtie
6、s.d(t)istheunknowninputsystemssuchasautomotive,manufacturingandrobots,hasvectorgeneratedagreatdealofresearchstudiesinthisarea,asin[1,Theobjectiveofthispaperistodeveloparobust2,31.Observer-basedFDmethodologyisoneofthemostnonlinearfaultdetectionarchitecturebased
7、onthesystemcommonlyusedFDstrategies,asin[4,5,61.Allmodel-basedmodeldescribedby(1)and(2).Throughoutthepaperwemethodsuseamodelofthemonitoredsystemtoproducethemakethefollowingassumptions:so-calledsymptomgenerator.IfthesystemisnotcomplexandA1d(t)anditsderivativear
8、ebounded,i.e.,canbedescribedaccuratelybythemathematicalmodel,FDisdirectlyperformedbyusingasimplegeometricalanalysisofresiduals.Inrealindustrialsystemshowever,themodelingerror,unkno
