简介:Thispaperdealswiththeiterativelearningcontrol(ILC)designformultiple-inputmultiple-output(MIMO),time-delaysystems(TDS).TwofeedbackILCschemesareconsideredusingtheso-calledtwo-dimensional(2D)analysisapproach.Itshowsthatcontinuous-discrete2DRoessersystemscanbedevelopedtodescribetheentirelearningdynamicsofbothILCschemes,basedonwhichnecessaryandsufficientconditionsfortheirstabilitycanbeprovided.Anumericalexampleisincludedtovalidatethetheoreticalanalysis.
简介:Inthispaper,iterativelearningcontrol(ILC)designisstudiedforaniteration-varyingtrackingprobleminwhichreferencetrajectoriesaregeneratedbyhigh-orderinternalmodels(HOIM).AnHOIMformulatedasapolynomialoperatorbetweenconsecutiveiterationsdescribesthechangesofdesiredtrajectoriesintheiterationdomainandmakestheiterativelearningproblembecomeiterationvarying.TheclassicalILCfortrackingiteration-invariantreferencetrajectories,ontheotherhand,isaspecialcaseofHOIMwherethepolynomialrenderstoaunitycoefficientoraspecialfirst-orderinternalmodel.ByinsertingtheHOIMintoP-typeILC,thetrackingperformancealongtheiterationaxisisinvestigatedforaclassofcontinuous-timenonlinearsystems.Time-weightednormmethodisutilizedtoguaranteevalidityofproposedalgorithminasenseofdata-drivencontrol.
简介:Arobustadaptiverepetitivelearningcontrolmethodisproposedforaclassoftime-varyingnonlinearsystems.Nussbaum-gainmethodisincorporatedintothecontroldesigntocounteractthelackofaprioriknowledgeofthecontroldirectionwhichdeterminesthemotiondirectionofthesystemunderanyinput.Itisshownthatthesystemstatecouldconvergetothedesiredtrajectoryasymptoticallyalongtheiterationaxisthroughrepetitivelearning.Simulationiscarriedouttoshowthevalidityoftheproposedcontrolmethod.