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外文翻译--PLC变频调速的网络反馈系统的实现.doc

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外文翻译--PLC变频调速的网络反馈系统的实现.doc

英文原文RealizationofNeuralNetworkInverseSystemwithPLCinVariableFrequencySpeedRegulatingSystemAbstract.Thevariablefrequencyspeedregulatingsystemwhichconsistsofaninductionmotorandageneralinverter,andcontrolledbyPLCiswidelyusedinindustrialfield..However,forthemultivariable,nonlinearandstronglycoupledinductionmotor,thecontrolperformanceisnotgoodenoughtomeettheneedsofspeedregulating.Themathematicmodelofthevariablefrequencyspeedregulatingsysteminvectorcontrolmodeispresentedanditsreversibilityhasbeenproved.Byconstructinganeuralnetworkinversesystemandcombiningitwiththevariablefrequencyspeedregulatingsystem,apseudolinearsystemiscompleted,andthenalinearcloseloopisdesignedtogethighperformance.UsingPLC,aneuralnetworkinversesystemcanberealizedinsystem.Theresultsofexperimentshaveshownthattheperformancesofvariablefrequencyspeedregulatingsystemcanbeimprovedgreatlyandthepracticabilityofneuralnetworkinversecontrolwastestified.1.IntroductionInrecentyears,withpowerelectronictechnology,microelectronictechnologyandmoderncontroltheoryinfiltratingintoACelectricdrivingsystem,invertershavebeenwidelyusedinspeedregulatingofACmotor.ThevariablefrequencyspeedregulatingsystemwhichconsistsofaninductionmotorandageneralinverterisusedtotaketheplaceofDCspeedregulatingsystem.Becauseofterribleenvironmentandseveredisturbanceinindustrialfield,thechoiceofcontrollerisanimportantproblem.Inreference123,Neuralnetworkinversecontrolwasrealizedbyusingindustrialcontrolcomputerandseveraldataacquisitioncards.Theadvantagesofindustrialcontrolcomputerarehighcomputationspeed,greatmemorycapacityandgoodcompatibilitywithothersoftwareetc.Butindustrialcontrolcomputeralsohassomedisadvantagesinindustrialapplicationsuchasinstabilityandfallibilityandworsecommunicationability.PLCcontrolsystemisspecialdesignedforindustrialenvironmentapplication,anditsstabilityandreliabilityaregood.PLCcontrolsystemcanbeeasilyintegratedintofieldbuscontrolsystemwiththehighabilityofcommunicationconfiguration,soitiswildlyusedinrecentyears,anddeeplywelcomed.Sincethesystemcomposedofnormalinverterandinductionmotorisacomplicatednonlinearsystem,traditionalPIDcontrolstrategycouldnotmeettherequirementforfurthercontrol.Therefore,howtoenhancecontrolperformanceofthissystemisveryurgent.Theneuralnetworkinversesystem45isanovelcontrolmethodinrecentyears.Thebasicideaisthatforagivensystem,aninversesystemoftheoriginalsystemiscreatedbyadynamicneuralnetwork,andthecombinationsystemofinverseandobjectistransformedintoakindofdecouplingstandardizedsystemwithlinearrelationship.Subsequently,alinearcloseloopregulatorcanbedesignedtoachievehighcontrolperformance.Theadvantageofthismethodiseasilytoberealizedinengineering.Thelinearizationanddecouplingcontrolofnormalsystemcanrealizeusingthismethod.CombiningtheneuralnetworkinverseintoPLCcaneasilymakeuptheinsufficiencyofsolvingtheproblemsofnonlinearandcouplinginPLCcontrolsystem.Thiscombinationcanpromotetheapplicationofneuralnetworkinverseintopracticetoachieveitsfulleconomic.Inthispaper,firstlytheneuralnetworkinversesystemmethodisintroduced,andmathematicmodelofthevariablefrequencyspeedregulatingsysteminvectorcontrolmodeispresented.Thenareversibleanalysisofthesystemisperformed,andthemethodsandstepsaregiveninconstructingNNinversesystemwithPLCcontrolsystem.Finally,themethodisverifiedintraditionalPIcontrolandNNinversecontrol.2.NeuralNetworkInverseSystemControlMethodThebasicideaofinversecontrolmethod6isthatforagivensystem,anαthintegralinversesystemoftheoriginalsystemiscreatedbyfeedbackmethod,andcombiningtheinversesystemwithoriginalsystem,akindofdecouplingstandardizedsystemwithlinearrelationshipisobtained,whichisnamedasapseudolinearsystemasshowninFig.1.Subsequently,alinearcloseloopregulatorwillbedesignedtoachievehighcontrolperformance.Inversesystemcontrolmethodwiththefeaturesofdirect,simpleandeasytounderstanddoesnotlikedifferentialgeometrymethod7,whichisdiscussestheproblemsingeometrydomain.Themainproblemistheacquisitionoftheinversemodelintheapplications.Sincenonlinearsystemisacomplexsystem,anddesiredstrictinverseisverydifficulttoobtain,evenimpossible.Theengineeringapplicationofinversesystemcontroldontmeettheexpectations.Asneuralnetworkhasnonlinearapproximateability,especiallyfornonlinearthepowerfultooltosolvetheproblem.a−thNNinversesystemintegratedinversesystemwithnonlinearabilityoftheneuralnetworkcanavoidthetroublesofinversesystemmethod.Thenitispossibletoapplyinversecontrolmethodtoacomplicatednonlinearsystem.a−thNNinversesystemmethodneedslesssysteminformationsuchastherelativeorderofsystem,anditiseasytoobtaintheinversemodelbyneuralnetworktraining.CascadingtheNNinversesystemwiththeoriginalsystem,apseudolinearsystemiscompleted.Subsequently,alinearcloseloopregulatorwillbedesigned.3.MathematicModelofInductionMotorVariableFrequencySpeedRegulatingSystemandItsReversibilityInductionmotorvariablefrequencyspeedregulatingsystemsuppliedbytheinverteroftrackingcurrentSPWMcanbeexpressedby5thordernonlinearmodelindqtwophaserotatingcoordinate.Themodelwassimplifiedasa3ordernonlinearmodel.Ifthedelayofinverterisneglected,themodelisexpressedasfollows1wheredenotessynchronousanglefrequency,andisrotatespeed.arestatorscurrent,andarerotorsfluxlinkageind,qaxis.isnumberofpoles.ismutualinductance,andisrotorsinductance.Jismomentofinertia.isrotorstimeconstant,andisloadtorque.Invectormode,thenSubstituteditintoformula1,then2Takingreversibilityanalysesofforum2,thenThestatevariablesarechosenasfollowsInputvariablesareTakingthederivativeonoutputinformula4,then56ThentheJacobimatrixisRealizationofNeuralNetworkInverseSystemwithPLC78Assoandsystemisreversible.RelativeorderofsystemisWhentheinverterisrunninginvectormode,thevariabilityoffluxlinkagecanbeneglectedconsideringthefluxlinkagetobeinvariablenessandequaltotherating.Theoriginalsystemwassimplifiedasaninputandanoutputsystemconcludedbyforum2.Accordingtoimplicitfunctionontologytheorem,inversesystemofformula3canbeexpressedas9Whentheinversesystemisconnectedtotheoriginalsysteminseries,thepseudolinearcompoundsystemcanbebuiltasthetypeof4.RealizationStepsofNeuralNetworkInverseSystem4.1AcquisitionoftheInputandOutputTrainingSamplesTrainingsamplesareextremelyimportantinthereconstructionofneuralnetworkinversesystem.Itisnotonlyneedtoobtainthedynamicdataoftheoriginalsystem,butalsoneedtoobtainthestaticdate.Referencesignalshouldincludealltheworkregionoforiginalsystem,whichcanbeensuretheapproximateability.Firstlythestepofactuatingsignalisgivencorrespondingevery10HZform0HZto50HZ,andtheresponsesofopenloopareobtain.Secondlyarandomtanglesignalisinput,whichisarandomsignalcascadingonthestepofactuatingsignalevery10seconds,andthecloseloopresponsesisobtained.Basedontheseinputs,1600groupstrainingsamplesaregotten.4.2TheConstructionofNeuralNetworkAstaticneuralnetworkandadynamicneuralnetworkcomposedofintegralisusedtoconstructtheinversesystem.Thestructureofstaticneuralnetworkis2neuronsininputlayer,3neuronsinoutputlayer,and12neuronsinhiddenlayer.Theexcitationfunctionofhiddenneuronismonotonicsmoothhyperbolictangentfunction.Theoutputlayeriscomposedofneuronwithlinearthresholdexcitationfunction.Thetrainingdatumarethecorrespondingspeedofopenloop,closeloop,firstorderderivativeofthesespeed,andsettingreferencespeed.After50timestraining,thetrainingerrorof

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