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.