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自适应预估控制及其在火电厂DCS中的应用 Title:AdaptiveEstimationandControlforPowerPlants'DistributedControlSystems(DCS) Introduction: Powerplantsplayacriticalroleinmeetingtheever-increasingenergydemand.Toensureoptimalandefficientoperation,controlsystemssuchastheDistributedControlSystems(DCS)areemployed.Theabilitytoaccuratelyestimateandcontrolvariousprocessvariableswithinthepowerplantiscrucialformaximizingefficiencyandminimizingemissions.Inrecentyears,adaptiveestimationandcontroltechniqueshavegainedsignificantattentionduetotheirabilitytohandleuncertaintiesandchangesinplantdynamics.ThispaperaimstoprovideanoverviewofadaptiveestimationandcontrolanditsapplicationinpowerplantDCS. 1.AdaptiveEstimationTechniques: 1.1Model-BasedAdaptiveEstimation:Thisapproachinvolvesutilizingamathematicalmodelofthepowerplantsystemtoestimatetheunknownoruncertainparameters.Model-basedestimationtechniquessuchastheExtendedKalmanFilter(EKF)andUnscentedKalmanFilter(UKF)havebeenwidelyusedinpowerplantDCStoestimatevariousprocessvariablesaccurately. 1.2Data-DrivenAdaptiveEstimation:Thisapproachreliesonhistoricaldataandmachinelearningalgorithmstoestimatetheuncertainparameters.TechniquessuchasArtificialNeuralNetworks(ANNs)andSupportVectorMachines(SVMs)havebeenemployedtotrainmodelsbasedonhistoricaldataandmakeaccurateestimationsinreal-time. 2.AdaptiveControlTechniques: 2.1ModelReferenceAdaptiveControl(MRAC):Thiscontroltechniqueusesaknownreferencemodeltoadaptivelyadjustthecontrolparameterstocompensateforuncertainties.MRAChasbeensuccessfullyutilizedinpowerplantDCStomaintainoptimalconditionsbycontinuouslylearningandupdatingthecontrolparameters. 2.2Model-FreeAdaptiveControl:Thiscontroltechniquedoesnotrelyonaspecificmodelofthepowerplantsystem.Instead,itutilizesonlinemeasurementsandoptimizationalgorithmstoadjustthecontrolactions.TechniquessuchasModel-FreeAdaptiveControl(MFAC)andReinforcementLearning(RL)haveshownpromisingresultsinpowerplantDCSbyprovidingonlineoptimizationandadaptationcapabilities. 3.ApplicationofAdaptiveEstimation