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面向驾驶极限的分布式驱动无人车辆鲁棒轨迹跟踪控制研究摘要:本文提出了一种面向驾驶极限的分布式驱动无人车辆鲁棒轨迹跟踪控制方法。该方法基于模型非线性控制理论,将车辆控制问题转化为一系列限制条件下的非线性优化问题,并提出一种改进的多模型自适应控制策略。利用该控制策略,无人车辆可以在复杂多变的路况条件下实现鲁棒轨迹跟踪,并兼顾了车辆的稳定性和动态性能。本文还给出了数值仿真实验结果,验证了所提出方法的可行性和有效性。
关键词:分布式驱动、无人车辆、鲁棒控制、轨迹跟踪、多模型自适应控制、非线性优化
Abstract:Thispaperproposesarobusttrajectorytrackingcontrolmethodfordistributeddrivingunmannedvehiclesfacingdrivinglimits.Basedonthemodelnonlinearcontroltheory,thevehiclecontrolproblemistransformedintoaseriesofnonlinearoptimizationproblemsunderconstraints,andanimprovedmulti-modeladaptivecontrolstrategyisproposed.Withthiscontrolstrategy,unmannedvehiclescanachieverobusttrajectorytrackingundercomplexandchangingroadconditions,whileconsideringthestabilityanddynamicperformanceofvehicles.Inaddition,numericalsimulationexperimentsareprovidedtoverifythefeasibilityandeffectivenessoftheproposedmethod.
Keywords:Distributeddriving,unmannedvehicles,robustcontrol,trajectorytracking,multi-modeladaptivecontrol,nonlinearoptimizationInrecentyears,thedevelopmentofautonomousdrivingtechnologyhasbecomeincreasinglypopular,asthedemandforsaferandmoreefficientdrivinghasgrown.However,thecomplexityandvariabilityofroadconditionsposesignificantchallengesforrobusttrajectorytrackingofunmannedvehicles.Toaddressthischallenge,anewrobustcontrolstrategyisproposedfordistributeddrivingofunmannedvehicles.
Theproposedcontrolstrategyisbasedonmulti-modeladaptivecontrol(MMAC)andnonlinearoptimizationtechniques.TheMMACmethodcombinesmultiplemodelstoimprovetheaccuracyofthecontrolsystemunderchangingroadconditions.Thenonlinearoptimizationapproachisusedtoachieveoptimalperformancewhileminimizingerrorsandensuringvehiclestability.
Thecontrolsystemofunmannedvehiclesisdividedintothreemainparts:pathplanning,trajectorytracking,andvehiclecontrol.Thepathplanningmodulegeneratesareferencetrajectorybasedonthedesiredrouteanddrivingconditions.Thetrajectorytrackingmoduleisresponsiblefortrackingthereferencetrajectoryaccuratelywhileaccountingforuncertaintiesinthevehicledynamicsandtheroadconditions.Finally,thevehiclecontrolmoduleadjuststhesteeringangle,vehiclespeed,andotherparameterstoachievestableandrobusttrajectorytracking.
Tovalidatethefeasibilityandeffectivenessoftheproposedcontrolstrategy,numericalsimulationsareconductedusingarealistic3Dvehiclemodelandavarietyofchallengingroadconditions.Thesimulationresultsshowthattheproposedmethodcanachieveaccurateandrobusttrajectorytrackingundervariousscenarios,includingsuddenobstacles,changingroadconditions,andcomplextrafficsituations.
Inconclusion,theproposedcontrolstrategyfordistributeddrivingofunmannedvehiclesusingMMACandnonlinearoptimizationtechniquescansignificantlyimprovethestability,safety,andaccuracyofautonomousdrivingsystems.Theproposedmethodhasthepotentialtobeappliedinvariousindustrialandcommercialdomains,suchastransportation,logistics,andautomateddeliveryservicesMoreover,thedevelopmentofautonomousdrivingtechnologycanalsohaveasignificantimpactonreducingtrafficcongestionandimprovingefficiencyontheroad.Withtheabilitytocommunicatewitheachotherandcooperateduringdriving,unmannedvehiclescanhelpeliminatetheneedforunnecessarylanechanges,reducethenumberofaccidentscausedbyhumanerror,andoptimizetrafficflow.
However,thewidespreadadoptionofautonomousdrivingtechnologyalsoraisesconcernsregardingjobdisplacementandcybersecurityrisks.Theimplementationofunmanneddrivingsystemscanpotentiallyreplacehumandriversanddisruptthetraditionaltransportationindustry,leadingtojoblossesandeconomicchallenges.Additionally,therelianceonadvancedcomputersystemsandconnectivityfeaturescanalsoincreasethevulnerabilityofthevehiclestocyber-attacksanddatabreaches.
Therefore,itiscrucialforpolicymakersandstakeholderstoconsiderthesocialandeconomicimplicationsofautonomousdrivingandcollaboratetoaddresspotentialrisksandchallengesassociatedwiththistechnology.Properregulations,standards,andeducationcanhelpensurethesafeandresponsibleintegrationofunmanneddrivingsystemsintosociety.
Overall,thedevelopmentofautonomousdrivingtechnologyusingMMACandnonlinearoptimizationtechniquescanoffersignificantbenefitsintermsofsafety,efficiency,andsustainability.Withfurtherresearchanddevelopment,unmannedvehicleshavethepotentialtotransformthewaywetravel,work,andliveinthefutureOnepotentialareaofgrowthforautonomousdrivingtechnologyisinthelogisticsandtransportationindustries.Unmannedtrucks,trains,andboatscouldrevolutionizethewaygoodsaremoved,reducingtheneedforhumandriversandpotentiallyimprovingefficiencyandsafety.Inaddition,unmannedsystemscouldallowfor24/7operation,increasingproductivityandminimizingdowntime.However,therearestillmanytechnicalandregulatorychallengesthatmustbeaddressedbeforethesesystemscanbewidelyadopted.
Anotherpotentialbenefitofautonomousdrivingtechnologyisimprovedaccessibilityforindividualswithdisabilitiesormobilitylimitations.Self-drivingcarscouldoffergreaterindependenceandmobilityforthosewhoareunabletodrivethemselves,allowingthemtotravelmoreeasilyandparticipatemorefullyinsociety.However,attentionmustalsobegiventoensuringthatthesetechnologiesareaccessibleandusableforindividualswithdifferenttypesofdisabilities.
Inadditiontothesepotentialbenefits,therearealsoconcernsabouttheimpactofautonomousdrivingtechnologyonemployment.Ifself-drivingvehiclesbecomewidespread,millionsofjobsinthetransportationandrelatedindustriescouldbelost.Thiscouldhavesignificantsocialandeconomicconsequences,andhighlightstheneedtocarefullymanagethetransitiontounmannedsystems.
Overall,thedevelopmentofautonomousdrivingtechnologyusingMMACandnonlinearoptimizationtechniquesholdsgreatpromiseforimprovingsafety,efficiency,andsustainabilityintransportation.However,therearemanytechnical,regulatory,andsocialchallengesthatmustbeaddressedinordertofullyrealizethepotential
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