伺服控制转矩脉动

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1、TorqueRippleMinimizationwithOn-lineParameterEstimationusingNeuralNetworksinPermanentMagnetSynchronousMotorsTongLiuIqbalHusainMalikElbulukMember,IEEESeniorMember,IEEEDepartmentofElectricalEngineeringTheUniversityofAkronAkron,OH44325-3904Phone:(330)-972-7649Fax:(330)-972-6487Abstract

2、-Aneuralnetworkbasedestimatorfortorquetuningoftheproportionalintegral(PI)gainsfordifferentconstantandstatorresistanceinpermanentmagnetoperatingpoints.Theiterationmodelingmethodsynchronousmotor(PMSM)ispresented.Theneuralpresentedin[4]isnotsufficientforestimatingthestatorweightsarein

3、itiallychosenrandomlyandamodelresistance,whichchangeswithtemperature.Wijenayakereferencealgorithmadjuststhoseweightstogivethe[5]neededastatorwindingtemperaturesensortooptimumestimations.Theq-axisinductanceismodeledfeedbackadigitaltemperaturevaluetotheparameteroff-lineaccordingtoq-a

4、xisstatorcurrent.Theneuralnetworkestimatorisabletotrackthevaryingparametersmodels.quiteaccuratelyatdifferentspeedswithconsistentOneoftheimportantapplicationsofparameterperformance.Theneuralnetworkparameterestimatorestimationisinthetorquerippleminimizationofthehasbeenappliedtoslotan

5、dfluxlinkagetorqueripplemotor.Holtz[6]builtanoff-lineflux-linkagetablewithminimizationofthePMSM.Thedrivesystemisrespecttorotorposition.Sincetherotorfluxlinkageinsensitivetotheseparameterchanges.Simulationtorqueripplesvarywithtemperatureandstatorcurrents,resultsjustifyingtheclaimare

6、presented.off-linemethodsarenoteffective.Adaptivetorquerippleminimizationpresentedin[7]onlydealtwithpartoftheI.INTRODUCTIONharmonicspresentinthetorqueripple.Thispaperpresentsanon-lineneuralnetworkThetorqueconstantk,(orrotorfluxlinkage),basedestimatorfortorqueconstantandstatorwindin

7、gstatorwindingresistanceandstatorinductanceareresistance.Furthermore,theq-axisstatorinductanceistime-varyingparametersinapermanentmagnetmodeledaccordingtotheq-axiscurrent.Theharmonicssynchronousmotor(PMSM).Accur,ateinformationofintherotorfluxlinkageisconsideredandtheallorsomeofthes

8、eparametersisnecessaryforp