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1、SCIENCECHINAInformationSciences.RESEARCHPAPER.January2017,Vol.60012202:1–012202:13doi:10.1007/s11432-015-0327-xRobuststateestimationforuncertainlinearsystemswithrandomparametricuncertaintiesHuaboLIU1,2*&TongZHOU1,31DepartmentofAutomation,TsinghuaUniversity,Beij
2、ing100084,China;2CollegeofAutomationandElectricalEngineering,QingdaoUniversity,Qingdao266071,China;3TsinghuaNationalLaboratoryforInformationScienceandTechnology(TNList),TsinghuaUniversity,Beijing100084,ChinaReceivedMarch1,2016;acceptedJune28,2016;publishedonlin
3、eNovember22,2016AbstractInthispaper,weinvestigatestateestimationsofadynamicalsystemwithrandomparametricuncertaintieswhichmayarbitrarilyaffectaplantstate-spacemodel.Arobustestimatorisderivedbasedonexpectationminimizationofestimationerrors.Ananalyticsolutionsimila
4、rtothatofthewell-knownKalmanfilterisderivedforthisnewrobustestimatorwhichcanberealizedrecursivelywithacomparablecomputa-tionalcomplexity.Undersomeweakassumptions,itisprovedthatthisestimatorconvergestoastablesystem,thecovariancematrixofestimationerrorsisbounded,a
5、ndtheestimationisasymptoticallyunbiased.Numer-icalsimulationsshowthattheobtainedrobustfilterhasanestimationaccuracycomparabletootherrobustestimatorsandcanbeappliedinawiderrange.Keywordsrobustness,stateestimation,recursiveestimation,parametricuncertainty,regulari
6、zedleast-squaresCitationLiuHB,ZhouT.Robuststateestimationforuncertainlinearsystemswithrandomparametricuncertainties.SciChinaInfSci,2017,60(1):012202,doi:10.1007/s11432-015-0327-x1IntroductionStateestimationhasbeenextensivelyutilizedinsystemcontrolandsignalproce
7、ssing.TheKalmanfilteristheoptimalestimatorunderthecriterionofmean-squaresforlinearsystemswithnormalexternaldisturbancesandwidelyappliedinnumerousfieldssuchascontrol,finance,communicationandsoon[1–3].Moreover,somevariantsofKalmanfilterareputforwardandapplied,suchase
8、xtendedKalmanfilterandunscentedKalmanfilter(see[1,4]andthereferencestherein).Sincemodellingerrorsaregenerallyunavoidableonaccountofcomplexmanufactureprocessandmodellinginaccur