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1、Proceedingsofthe2001IEEEInternationalSymposiumonIntelligentControlSeptember57,2001MBxicoCity,MBxicoANADAPTIVENEURALCONTROLOFADCMOTORIEROHAMS.BARUCHRUBENGARRIDOJOSE-MARTINFLORESJUAN-CARLOSMARTINEZDepartmentofAutomaticControl,CINVESTAV-IPN,AV.IPNNo2508,A.P.14740,MexicoD.
2、F.07360,MEXICO,Fax:(+52)5747-7089,E-mail:baruch@ctrl.cinvestav.mxAbstract.ARecurrentTrainableNeuralNetwork(RTNN)togetherwithaBackpropagationTrough-Time(BFTT)learningalgorithmareappliedforareal-timeidentificationandadaptivecontrolofaDC-motordrive.Thepaperproposestouseth
3、reeRTNNs,separatelyforthepartsofthesystemsidentification,thestatefeedbackcontrolandthefeedforwardcontrol.TheappliedRTNNmodelhasaminimumnumberofparametersduetoitsJordancanonicalstructure,whichpermitstousethegeneratedvectorofstatesdirectlyforaDC-motorfeedbackcontrol.Thee
4、xperimentalresults,confirmtheapplicabilityofthedescribedidentificationandcontrolmethodologyinpracticeandalsoconfirmthegoodqualityoftheRTNN.KeyWords:DC-Motor,RecurrentNeuralNetwork,BackpropagationTroughTimeLearning,SystemsIdentification,AdaptiveControl[13,[4].Thepresenc
5、eofnonlinearfrictionforcesisunavoidableinhighperformancemotioncontrol1.INTRODUCTIONsystem.Inservosystems,ifthecontrollerisRecentdevelopmentsinscienceandtechnologydesignedwithoutconsiderationofthefriction,theprovideawidescopeofapplicationsofhighclosed-loopsystemmayshows
6、teady-statetrackingperformanceelectricmotordrivesinareasinvolvingerrorandoroscillations.Inaddition,thefrictionmechatronics,suchasrobotics,rollingmills,characteristicsmaychangeeasilyduetothemachinetools,etc.,whereanaccuratespeedorenvironment’schangeslikeloadvariations,p
7、ositioncontrolisofcriticalimportance.Therearetemperatureandhumiditychanges,andsomeanincreasingnumberofapplicationsinhighdynamiceffectscouldbeobserved,[l].So,theprecisionmotioncontrolsystemsinmanufacturing,standardPIDtypeservocontrolalgorithmisnoti.e.,ultraprecisionmach
8、ining,assemblyofsmallcapableofdeliveringthedesireprecisionunderthecomponentsandmicrodrives.Itisverydifficulttoinfluen
