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基于EKF和Lyapunov函数的移动机器人轨迹跟踪控制(英文).docx

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基于EKF和Lyapunov函数的移动机器人轨迹跟踪控制(英文) Introduction Mobilerobotsarebecomingincreasinglypopularinvariousfields,suchasmanufacturing,transportation,andservicerobotics.Precisecontroloftherobot'smovementisessentialinmanyapplications.Trajectorytrackingisoneofthecriticaltasksthatamobilerobotneedstoperformaccurately.Thetrajectorytrackingcontrolhasattractednumerousresearchstudiesinrecentyearsthathaveresultedinvariouscontrolstrategies.ThispaperdiscussestheEKFandLyapunovfunction-basedtrajectorycontrolformobilerobots. EKF-BasedTrajectoryControl ExtendedKalmanFilter(EKF)isawell-knownrecursiveestimationmethodthatiscommonlyusedinmobilerobotnavigationapplications.EKFusesaniterativeprocessforestimationthatincorporatesbothsystemdynamicsandmeasurements.TheEKFestimatesarobot'sstatevariables,includingpositionandorientation,inreal-timebasedonsensormeasurements.TheEKFalgorithm'sprimarystepsarepredictionandupdate.Inthepredictionstep,therobot'sstateispredictedbasedonthecontrolinputandthepreviousstateestimate.Intheupdatestep,thestateiscorrectedbasedonthesensormeasurement. TheEKFalgorithmcanbeusedfortrajectorytrackingbydesigningthecontrolinputbasedontheestimationoftherobot'sstate.Thedesiredtrajectorycanbespecifiedintermsofposition,velocity,andacceleration.Thecontrolinputisgeneratedbasedontheestimatedstate,whichminimizesthedifferencebetweentheactualanddesiredtrajectory. TheEKF-basedtrajectorytrackingcontrolhasseveraladvantages.TheEKFalgorithmprovidesarobustandreliableestimationoftherobot'sstate,eveninthepresenceofsensornoiseanduncertainty.TheEKF-basedcontrolalsoallowsfortrackingofnon-linearandcomplextrajectories. LyapunovFunction-BasedTrajectoryControl Lyapunovfunctionsarecommonlyusedincontroltheorytoanalyzethestabilityofasystem.ALyapunovfunctionisascalarfunctionthatsatisfiescertainconditions,wherethefunctionvaluedecreasesovertimeforthesystemtrajectories.Lyapunovfunctionsareutilizedindesigningcontrolalgorithmsthatensuresystemstabilityandconvergencetoequilibriumpoints. Lyapunovfunction-basedtrajector