外文翻译PLC变频调速的网络反馈系统的实现.docx

外文翻译PLC变频调速的网络反馈系统的实现.docx

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外文翻译PLC变频调速的网络反馈系统的实现

RealizationofNeuralNetworkInverseSystemwithPLCinVariableFrequencySpeed-RegulatingSystem

Abstract.Thevariablefrequencyspeed-regulatingsystemwhichconsistsofaninductionmotorandageneralinverter,andcontrolledbyPLCiswidelyusedinindustrialfield..However,forthemultivariable,nonlinearandstronglycoupledinductionmotor,thecontrolperformanceisnotgoodenoughtomeettheneedsofspeed-regulating.Themathematicmodelofthevariablefrequencyspeed-regulatingsysteminvectorcontrolmodeispresentedanditsreversibilityhasbeenproved.Byconstructinganeuralnetworkinversesystemandcombiningitwiththevariablefrequencyspeed-regulatingsystem,apseudo-linearsystemiscompleted,andthenalinearclose-loopadjustorisdesignedtogethighperformance.UsingPLC,aneuralnetworkinversesystemcanberealizedinacturalsystem.Theresultsofexperimentshaveshownthattheperformancesofvariablefrequencyspeed-regulatingsystemcanbeimprovedgreatlyandthepracticabilityofneuralnetworkinversecontrolwastestified.

1.Introduction

Inrecentyears,withpowerelectronictechnology,microelectronictechnologyandmoderncontroltheoryinfiltratingintoACelectricdrivingsystem,invertershavebeenwidelyusedinspeed-regulatingofACmotor.Thevariablefrequencyspeed-regulatingsystemwhichconsistsofaninductionmotorandageneralinverterisusedtotaketheplaceofDCspeed-regulatingsystem.Becauseofterribleenvironmentandseveredisturbanceinindustrialfield,thechoiceofcontrollerisanimportantproblem.Inreference[1][2][3],Neuralnetworkinversecontrolwasrealizedbyusingindustrialcontrolcomputerandseveraldataacquisitioncards.Theadvantagesofindustrialcontrolcomputerarehighcomputationspeed,greatmemorycapacityandgoodcompatibilitywithothersoftwareetc.Butindustrialcontrolcomputeralsohassomedisadvantagesinindustrialapplicationsuchasinstabilityandfallibilityandworsecommunicationability.PLCcontrolsystemisspecialdesignedforindustrialenvironmentapplication,anditsstabilityandreliabilityaregood.PLCcontrolsystemcanbeeasilyintegratedintofieldbuscontrolsystemwiththehighabilityofcommunicationconfiguration,soitiswildlyusedinrecentyears,anddeeplywelcomed.Sincethesystemcomposedofnormalinverterandinductionmotorisacomplicatednonlinearsystem,traditionalPIDcontrolstrategycouldnotmeettherequirementforfurthercontrol.Therefore,howtoenhancecontrolperformanceofthissystemisveryurgent.

Theneuralnetworkinversesystem[4][5]isanovelcontrolmethodinrecentyears.Thebasicideaisthat:

foragivensystem,aninversesystemoftheoriginalsystemiscreatedbyadynamicneuralnetwork,andthecombinationsystemofinverseandobjectistransformedintoakindofdecouplingstandardizedsystemwithlinearrelationship.Subsequently,alinearclose-loopregulatorcanbedesignedtoachievehighcontrolperformance.Theadvantageofthismethodiseasilytoberealizedinengineering.Thelinearizationanddecouplingcontrolofnormalnonlinear

systemcanrealizeusingthismethod.

CombiningtheneuralnetworkinverseintoPLCcaneasilymakeuptheinsufficiencyofsolvingtheproblemsofnonlinearandcouplinginPLCcontrolsystem.Thiscombinationcanpromotetheapplicationofneuralnetworkintopracticetoachieveitfulleconomicandsocialbenefits

Inthispaper,firstlytheneuralnetworkinversesystemmethodisintroduced,andmathematicmodelofthevariablefrequencyspeed-regulatingsysteminvectorcontrolmodeispresented.Thenareversibleanalysisofthesystemisperformed,andthemethodsandstepsaregiveninconstructingNN-inversesystemwithPLCcontrolsystem.Finally,themethodisverifiedinexperiments,andcomparedwith

traditionalPIcontrolandNN-inversecontrol.

2.NeuralNetworkInverseSystemControlMethod

Thebasicideaofinversecontrolmethod[6]isthat:

foragivensystem,anα-thintegralinversesystemoftheoriginalsystemiscreatedbyfeedbackmethod,andcombiningtheinversesystemwithoriginalsystem,akindofdecouplingstandardizedsystemwithlinearrelationshipisobtained,whichisnamedasapseudolinearsystemasshowninFig.1.Subsequently,alinearclose-loopregulatorwillbedesignedtoachievehighcontrolmathematicmodelofthevariableperformance.

Inversesystemcontrolmethodwiththefeaturesofdirect,simpleandeasytounderstanddoesnotlikedifferentialgeometrymethod[7],whichisdiscussestheproblemsin"geometrydomain".Themainproblemistheacquisitionoftheinversemodelintheapplications.Sincenon-linearsystemisacomplexsystem,anddesiredstrictanalyticalinverseisvery

obtain,evenimpossible.Theengineeringapplicationofinversesystemcontroldoesn’tmeettheexpectations.Asneuralnetworkhasnon-linearapproximateability,especiallyfornonlinearcomplexitysystem,itbecomeswiththepowerfulexpectationstooltosolvetheproblem.

a−thNNinversesystemintegratedinversesystemwithnon-linearabilityoftheneuralnetworkcanavoidthetroublesofinversesystemmethod.Thenitispossibletoapplyinversecontrolmethodtoacomplicatednon-linearsystem.a−thNNinversesystemmethodneedslesssysteminformationsuchastherelativeorderofsystem,anditiseasytoobtaintheinversemodelbyneuralnetworktraining.CascadingtheNNinversesystemwiththeoriginalsystem,apseudo-linearsystemiscompleted.Subsequently,alinearclose-loopregulatorwillbedesigned.

3.MathematicModelofInductionMotorVariableFrequency

Speed-RegulatingSystemandItsReversibility

Inductionmotorvariablefrequencyspeed-regulatingsystemsuppliedbytheinverteroftrackingcurrentSPWMcanbeexpressedby5-thordernonlinearmodelind-qtwo-phaserotatingcoordinate.Themodelwassimplifiedasa3-ordernonlinearmodel.Ifthedelayofinverterisneglectedsystemoriginalsystem,themodelisexpressedasfollows:

（1）

where

denotessynchronousanglefrequency,and

isrotatespeed.

arestator’scurrent,and

arerotor’sfluxlinkagein

（d,q）axis.

isnumberofpoles.

ismutualinductance,and

isrotor’sinductance.Jismomentofinertia.

isrotor’stimeconstant,and

isloadynchronousanglefrequencytorque.Invectormode,then

Substituteditintoformula

（1）,then

（2）

Takingreversibilityanalysesofforum

（2）,then

Thestatevariablesarechosenasfollows

Inputvariablesare

Takingthederivativeonoutputinformula（4）,then

（5）

（6）

ThentheJacobimatrixisRealizationofNeuralNetworkInverseSystemwithPLC

（7）

（8）

As

so

andsystemisreversible.Relative-orderofsystemis

Whentheinverterisrunninginvectormode,thevariabilityoffluxlinkagecanbeneglected（consideringthefluxlinkagetobeinvariablenessandequaltotherating）.Theoriginalsystemwassimplifiedasaninputandanoutputsystemconcludedbyforum

（2）.

Accordingtoimplicitfunctionontologytheorem,inversesystemofformula（3）

canbeexpressedas

（9）

Whentheinversesystemisconnectedtotheoriginalsysteminseries,thepseudolinearcompoundsystemcanbebuiltasthetypeof

4.RealizationStepsofNeuralNetworkInverseSystem

4.1AcquisitionoftheInputandOutputTrainingSamples

Trainingsamplesareextremelyimportantinthereconstructionofneuralnetworkinversesystem.Itisnotonlyneedtoobtainthedynamicdataoftheoriginalsystem,butalsoneedtoobtainthestaticdate.Referencesignalshouldincludealltheworkregionoforiginalsystem,whichcanbeensuretheapproximateability.Firstlythestepofactuatingsignalisgivencorrespondingevery10HZform0HZto50HZ,andtheresponsesofopenloopareobtain.Secondlyarandomtanglesignalisinput,whichisarandomsignalcascadingonthestepofactuatingsignalevery10seconds,andthecloseloopresponsesisobtained.Basedontheseinputs,1600groupsshouldincludealltrainingsamplesaregotten.

4.2TheConstructionofNeuralNetwork

Astaticneuralnetworkandadynamicneuralnetworkcomposedofintegralisusedtoconstructtheinversesystem.Thestructureofstaticneuralnetworkis2neuronsininputlayer,3neuronsinoutputlayer,and12neuronsinhiddenlayer.Theexcitationfunctionofhiddenneuronismonotonicsmoothhyperbolictangentfunction.Theoutputlayeriscomposedofneuronwithlinearthresholdexcitationfunction.Thetrainingdatumarethecorrespondingspeedofopen-loop,close-loop,firstorderderivativeofthesespeed,andsettingreferencespeed.After50timestraining,thetrainingerrorofneuralnetworkachievesto0.001.Theweightandthresholdoftheneuralnetworkaresaved.Theinversemodelandadynamicneuralnetworkcomposedoforiginalsystemisobtained.

5.ExperimentsandResults

5.1HardwareoftheSystem

ThehardwareoftheexperimentsystemisshowninFig5.ThehardwaresystemincludesuppercomputerinstalledwithSupervisory&ControlconfigurationsoftwareWinCC6.0[8],andS7-300PLCofSIEMENS,inverter,inductioninstalledwithmotorandControlphotoelectriccoder.

PLCcontrollerchoosesS7-315-2DP,whichhasaPROFIBUS-DPinterfaceandaMPIinterface.SpeedacquisitionmoduleisFM350-1.WinCCisconnectedwiththeexperimentsystemS7-300byCP5611usingMPIprotocol.

ThetypeofinverterisMMVofSIEMENS.ItcancommunicatewithSIEMENSPLCbyUSSprotocol.ACB15moduleisaddedontheinverterinthissystem.

5.2SoftwareProgram

5.2.1CommunicationIntroduction

MPI（MultiPointInterface）isasimpleandinexpensivecommunicationstrategyusinginslowlyandnon-largedatatransformingfield.Thed