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AMT离合器位置闭环PID控制参数优化Title:OptimizationofPIDControlParametersforAMTClutchPositionClosed-LoopControlAbstract:TheAutomatedManualTransmission(AMT)systemplaysacrucialroleinachievingefficientandsmoothgearshiftinginmodern-dayvehicles.Toensureaccurateandreliablecontrol,itisessentialtooptimizetheProportional-Integral-Derivative(PID)controlparametersusedintheclosed-loopcontroloftheAMTclutchposition.ThispaperaimstoexplorevariousmethodsforoptimizingthePIDcontrolparameterstoenhancetheperformanceoftheAMTclutchpositioncontrolsystem.1.IntroductionTheclutchpositioncontrolinanAMTsystemiscriticalforachievingsmoothgearshifting.Intraditionalmanualtransmissions,theclutchpositioniscontrolledmanuallybythedriver.However,inAMTsystems,itiscontrolledautomatically,necessitatingtheuseofaclosed-loopcontrolsystem.ThePIDcontrollerisawidelyadoptedcontrolalgorithmduetoitssimplicityandeffectiveness.2.PIDControllerBasicsThePIDcontrollerconsistsofthreeparameters:proportionalgain(Kp),integralgain(Ki),andderivativegain(Kd).Theproportionaltermprovidesanimmediateresponsetoanyerror,theintegraltermeliminatessteady-stateerror,andthederivativetermhelpsreduceovershootandimprovesystemstability.Tuningtheseparametersiscrucialforoptimizingperformance.3.ExistingMethodsforPIDParameterOptimizationa)ManualTuning:Thismethodinvolvestrialanderror,wheretheengineermanuallyadjuststhecontrolparametersuntilthedesiredresponseisachieved.Whilesimple,thisapproachcanbetime-consumingandinefficient.b)Ziegler-NicholsMethod:ThismethodisapopularapproachfortuningPIDcontrollers.Itinvolvesdeterminingtheultimategain(Ku)andultimateperiod(Tu)throughexperimentationandcalculatingthePIDparametersbasedonpredefinedrules.However,thismethodmayresultinsuboptimalperformance.c)GeneticAlgorithms:Geneticalgorithmsareoptimizationtechniquesinspiredbynaturalevolutionprocesses.TheyinvolvedefiningafitnessfunctionanditerativelysearchingfortheoptimalPIDparameters.Geneticalgorithmsoffertheadvantageofautomationandcanyieldexcellentresults,buttheycanbecomputationallyintensive.4.ProposedOptimizationApproachInthispaper,anoveloptimizationapproachforthePIDcontrolparametersoftheAMTclutchpositionisproposed.Theapproachleveragesacombinationofsystemidentification,particleswarmoptimization(PSO),andmachinelearningtechniques.a)SystemIdentification:AnaccuratemathematicalmodeloftheAMTclutchsystemisdevelopedusingsystemidentificationtechniques.Thismodelservesasthebasisforsubsequentoptimization.b)ParticleSwarmOptimization:PSOisapopulation-basedstochasticoptimizationtechniqueinspiredbythebehaviorofbirdflocking.Itinvolvesiterativelyadjustingapopulationofcandidatesolutionstofindtheoptimalsolution.Inthisapproach,PSOisutilizedtooptimizethePIDcontrolparametersbasedonapredefinedfitnessfunctionthatcapturesthedesiredresponsecharacteristics.c)MachineLearning-basedFine-tuning:OncetheinitialPIDcontrolparametersareoptimizedusingPSO,amachinelearning-basedfine-tuningapproachisemployed.ThisstepinvolvescollectingdatafromtheAMTsystemduringreal-timeoperationandutilizingmachinelearningalgorithmstofurtherrefinethePIDcontrolparameters.Themachinelearningmodelcontinuouslylearnsandadaptstothedynamicbehaviorofthesystem,enhancingperformance.5.ResultsandDiscussionTheproposedoptimizationapproachisimplementedandtestedusingasimulatedAMTsystem.Theperformanceofthesystemisevaluatedbasedonvariousperformancemetrics,suchassettlingtime,overshoot,andrisetime.Theresultsdemonstratesignificantimprovementscomparedtoconventionalmethods,suchasmanualtuningandtheZiegler-Nicholsmethod.6.ConclusionThispaperpresentsacomprehensiveapproachforoptimizingthePIDcontrolparametersforAMTclutchpositioncontrol.Thecombinationofsystemidentification,PSO,andmachinelearningtechniquesoffersarobustandefficientapproachthatsignificantlyenhancestheperformanceoftheAMTclutchpositioncontrolsystem.TheproposedapproachcanbeappliedtopracticalAMTsystemstoimprovegearshiftingefficiencyandoverallvehicleperformance.