简介:Thispaperstudiestheglobalrobustoutputregulationproblemforlowertriangularsystemssubjecttononlinearexosystems.Byemployingtheinternalmodelapproach,thisproblemcanbeboileddowntoaglobalrobuststabilizationproblemofatime-varyingnonlinearsysteminthecascade-connectedform.Then,asetofsufficientconditionsforthesolvabilityoftheproblemisderived,andthus,leadingtothesolutiontotheglobalrobustoutputregulationproblem.Anapplicationofthemainresultofthispaperisalsoproposed.
简介:Theaimofthispaperistostudythepracticalф0-stabilityinprobability(Pф0SiP)andpracticalф0-stabilityinpthmean(Pф0SpM)ofswitchedstochasticnonlinearsystems.Sufficientconditionsonsuchpracticalpropertiesareobtainedbyusingthecomparisonprincipleandthecone-valuedLyapunovfunctionmethods.Also,basedonanextendedcomparisonprinciple,aperturbationtheoryofswitchedstochasticsystemsisgiven.
简介:Inthispaper,westudythecontrollabilityresultsforthenonlinearimpulsiveintegrodifferentialevolutionsystemswithtime-varyingdelaysinBanachspaces.Thesufficientconditionsofexactcontrollabilityisprovedunderwithoutassumingthecompactnessoftheevolutionoperator.TheresultsareobtainedbyusingthesemigrouptheoryandtheSchaferfixedpointtheorem.
简介:Inthispaper,theglobalasymptoticstabilizationbyoutputfeedbackisinvestigatedforaclassofuncertainnonlinearsystemswithunmeasuredstatesdependentgrowth.Comparedwiththecloselyrelatedworks,theremarkablenessofthepaperisthateitherthegrowthrateisanunknownconstantorthedimensionoftheclosed-loopsystemissignificantlyreduced,mainlyduetotheintroductionofadistinctdynamichigh-gainobserverbasedonanewupdatinglaw.Motivatedbytherelatedstabilizationresults,andbyskillfullyusingthemethodsofuniversalcontrolandbackstepping,weobtainthedesignschemetoanadaptiveoutput-feedbackstabilizingcontrollertoguaranteetheglobalasymptoticstabilityoftheresultingclosed-loopsystem.Additionally,anumericalexampleisconsideredtodemonstratetheeffectivenessoftheproposedmethod.
简介:Thispaperisconcernedwiththeglobalstabilizationviaoutput-feedbackforaclassofhigh-orderstochasticnonlinearsystemswithunmeasurablestatesdependentgrowthanduncertaincontrolcoefficients.Indeed,therehavebeenabundantdeterministicresultswhichrecentlyinspiredtheintenseinvestigationfortheirstochasticanalogous.However,becauseofthepossibilityofnon-uniquesolutionstothesystems,therelackbasicconceptsandtheoremsfortheproblemunderinvestigation.Firstofall,twostochasticstabilityconceptsaregeneralizedtoallowthestochasticsystemswithmorethanonesolution,andakeytheoremisgiventoprovidethesufficientconditionsforthestochasticstabilitiesinaweakersense.Then,byintroducingthesuitablereducedorderobserverandappropriatecontrolLyapunovfunctions,andbyusingthemethodofaddingapowerintegrator,acontinuous(nonsmooth)output-feedbackcontrollerissuccessfullydesigned,whichguaranteesthattheclosed-loopsystemisgloballyasymptoticallystableinprobability.
简介:Arobustadaptiverepetitivelearningcontrolmethodisproposedforaclassoftime-varyingnonlinearsystems.Nussbaum-gainmethodisincorporatedintothecontroldesigntocounteractthelackofaprioriknowledgeofthecontroldirectionwhichdeterminesthemotiondirectionofthesystemunderanyinput.Itisshownthatthesystemstatecouldconvergetothedesiredtrajectoryasymptoticallyalongtheiterationaxisthroughrepetitivelearning.Simulationiscarriedouttoshowthevalidityoftheproposedcontrolmethod.
简介:Thispaperproposesanewasymptoticattitudetrackingcontrollerforanunderactuated3-degree-of-freedom(DOF)laboratoryhelicoptersystembyusinganonlinearrobustfeedbackandaneuralnetwork(NN)feedforwardterm.Thenonlinearrobustcontrollawisdevelopedthroughamodifiedinner-outerloopapproach.TheapplicationoftheNN-basedfeedforwardistocompensateforthesystemuncertainties.Theproposedcontroldesignstrategyrequiresverylimitedknowledgeofthesystemdynamicmodel,andachievesgoodrobustnesswithrespecttosystemparametricuncertainties.ALyapunov-basedstabilityanalysisshowsthattheproposedalgorithmscanensureasymptotictrackingofthehelicopter'selevationandtravelmotion,whilekeepingthestabilityoftheclosed-loopsystem.Real-timeexperimentresultsdemonstratethatthecontrollerhasachievedgoodtrackingperformance.