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基于自适应扩展卡尔曼滤波的消能减震结构及附加阻尼力识别.docx

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基于自适应扩展卡尔曼滤波的消能减震结构及附加阻尼力识别 Abstract AdaptiveExtendedKalmanFilter(AEKF)isawidelyusedmethodfordynamicsystemestimationandcontrol.Inthispaper,anovelAEKF-basedapproachforenergydissipationandvibrationreductionofstructuralsystemsisproposed.Themethodinvolvesreal-timeestimationofthestateofthestructuralsystem,predictionofitsfuturebehaviorandoptimalcontrolactions.TheAEKFalgorithmisemployedtoupdatetheestimationofthesystem'sstatebasedonnoisymeasurementsobtainedfromsensorsinstalledonthestructure.Toimprovetheaccuracyandrobustnessoftheestimation,anovelfilteringtechniqueisproposedwhichadaptstheestimationcovariancematrixbasedonthesystem'sbehavior.Additionally,anadditivedampingforceisintroducedtofurtherenhancetheenergydissipationandvibrationreductionofthestructure.AnewidentificationtechniqueisproposedtoestimatethedampingforcebyusingtheKalmanfilter.Simulationstudiesareconductedtodemonstratetheeffectivenessoftheproposedmethodinreducingthestructuralvibrationanddissipatingenergyduringseismicevents. Keywords:AdaptiveExtendedKalmanFilter,energydissipation,vibrationreduction,structuralsystem,dampingforceidentification,seismicevents. Introduction Dynamicresponseandseismicperformanceofstructuresarecriticalinensuringthesafetyofbuildingoccupantsandpreventingdamagetoinfrastructure.Traditionalpassivecontrolmethodssuchasbaseisolation,tunedmassdampers,anddampersareusedtoreducethedynamicresponseofstructures.However,thesedevicesarelimitedintheireffectivenessastheycannotadapttochangingdynamicloadsandvariationsinstructuralproperties.Toaddressthisissue,activecontrolmethodshavebeendevelopedwhichusecontrolalgorithmstomanipulatethestructuralresponsetodynamicloadsinreal-time.Thisapproachallowsformoreeffectiveandadaptivecontrolofstructuralresponsesduringearthquakesandothernaturaldisasters. OneofthemostwidelyusedactivecontrolmethodsistheAdaptiveExtendedKalmanFilter(AEKF).TheAEKFisapowerfultoolforestimatingthestateofdynamicsystemsbasedonnoisysensormeasurements.Itupdatesitsestimatesinreal-timeusingarecursivealg