基于線結(jié)構(gòu)光和優(yōu)化PID的剛?cè)狁詈蠙C械臂振動控制研究

基于線結(jié)構(gòu)光和優(yōu)化PID的剛?cè)狁詈蠙C械臂振動控制研究基于線結(jié)構(gòu)光和優(yōu)化PID的剛?cè)狁詈蠙C械臂振動控制研究

摘要：本文提出了一種基于線結(jié)構(gòu)光和優(yōu)化PID的剛?cè)狁詈蠙C械臂振動控制方法，旨在減小機械臂運動過程中的振動幅度，提高控制精度和穩(wěn)定性，為工業(yè)機器人的應(yīng)用提供技術(shù)支持。首先，基于線結(jié)構(gòu)光技術(shù)實現(xiàn)機械臂姿態(tài)測量，提取關(guān)鍵點位移信息，并建立起機械臂運動模型。然后，針對機械臂在運動過程中的振動問題，設(shè)計了基于優(yōu)化PID控制器的控制方法。在控制器參數(shù)優(yōu)化方面，采用了遺傳算法和粒子群優(yōu)化方法，提高了控制器的響應(yīng)速度和抗干擾能力。最后，通過實驗驗證，證明了該控制方法的有效性和實用性，實驗結(jié)果表明，相比傳統(tǒng)PID控制，該控制方法成功降低了機械臂的振動幅度，提高了機械臂的穩(wěn)定性和精度。

關(guān)鍵詞：線結(jié)構(gòu)光、剛?cè)狁詈蠙C械臂、振動控制、PID控制、優(yōu)化算法。

Abstract:Thispaperproposesavibrationcontrolmethodforrigid-flexiblecoupledmechanicalarmbasedonlinestructurelightandoptimizedPIDcontrol.Theaimistoreducethevibrationamplitudeduringthemovementofthemechanicalarm,improvethecontrolprecisionandstability,andprovidetechnicalsupportforindustrialrobotapplication.Firstly,themechanicalarmattitudeismeasuredbasedonthelinestructurelighttechnology,thekeypointdisplacementinformationisextracted,andthemechanicalarmmotionmodelisestablished.Then,aimingatthevibrationproblemofthemechanicalarmduringthemovement,acontrolmethodbasedonoptimizedPIDcontrollerisdesigned.Intermsofcontrollerparameteroptimization,geneticalgorithmandparticleswarmoptimizationmethodareadoptedtoimprovetheresponsespeedandanti-interferenceabilityofthecontroller.Finally,throughexperimentalverification,theeffectivenessandpracticalityofthecontrolmethodareproved.TheexperimentalresultsshowthatcomparedwithtraditionalPIDcontrol,thecontrolmethodsuccessfullyreducesthevibrationamplitudeofthemechanicalarmandimprovesthestabilityandaccuracyofthemechanicalarm.

keywords:linestructurelight,rigid-flexiblecoupledmechanicalarm,vibrationcontrol,PIDcontrol,optimizationalgorithmInrecentyears,therehasbeenagrowingdemandforhigh-precisionandflexiblemechanicalarmsinvariousindustries.However,thevibrationofthemechanicalarmduringoperationcansignificantlyaffectitsstability,accuracyandspeed,leadingtopoorperformanceandpotentialsafetyhazards.Therefore,itisessentialtodevelopeffectivevibrationcontrolstrategiesformechanicalarms.

Amongvariousvibrationcontrolmethods,thePIDcontrolhasbeenwidelyusedduetoitssimplicity,effectivenessandlowcost.However,traditionalPIDcontrolcannoteffectivelysuppressthevibrationoftherigid-flexiblecouplingmechanicalarm,whichisacommontypeofmechanicalarm.Therefore,anoptimization-basedPIDcontrolmethodisproposedinthispapertoimprovethevibrationsuppressionperformanceofthemechanicalarm.

Theproposedcontrolmethodcombineslinestructurelighttechnologyandoptimizationalgorithmtoachieveaccuratetrackingofthemechanicalarmtiptrajectoryandrobustvibrationsuppression.Thelinestructurelighttechnologyisusedtoobtainthepositionandorientationinformationofthemechanicalarmtipinreal-time,whichisfedbacktothecontroller.TheoptimizationalgorithmisemployedtosearchfortheoptimalPIDcontrolparametersthatmaximizethecontrolperformanceandminimizethevibrationamplitudeofthemechanicalarm.

Toevaluatetheeffectivenessandpracticalityofthecontrolmethod,experimentswereconductedonaflexiblemechanicalarm.Theexperimentalresultsshowthattheproposedcontrolmethodsuccessfullyreducesthevibrationamplitudeofthemechanicalarmandimprovesitsstabilityandaccuracy.ComparedwithtraditionalPIDcontrol,theproposedmethodachievesbettervibrationsuppressionperformanceandcaneffectivelyadapttodifferentoperatingconditions.

Inconclusion,theoptimization-basedPIDcontrolmethodproposedinthispaperprovidesapracticalsolutionforvibrationcontrolofrigid-flexiblecoupledmechanicalarms.Itcansignificantlyimprovetheperformanceandsafetyofmechanicalarmsinvariousapplications.FutureresearchcanfurtherrefineandoptimizethecontrolmethodandextendittomorecomplexmechanicalsystemsMoreover,besidestheoptimization-basedPIDcontrolmethod,therearealsoothercontrolstrategiesthatcanbeappliedinthevibrationcontrolofmechanicalsystems,suchasfuzzycontrol,neuralnetworkcontrol,andadaptivecontrol.Fuzzycontrolisamethodthatutilizesfuzzylogictodealwithcomplexanduncertainsystems.Itcaneffectivelyhandlenonlinearsystemswithimpreciseandincompleteinformation.Neuralnetworkcontrolisamethodthatusesartificialneuralnetworkstoapproximatethesystemdynamicsandobtainacontroloutput.Itcanachievehighprecisioncontrolandadaptabilitytovaryingconditions.Adaptivecontrolisamethodthatadjuststhecontrolparametersbasedonthesystem'sbehaviorandresponse.Itcanimprovethecontrolperformanceandstabilityofthesystem.

Furthermore,withthedevelopmentofadvancedsensingandactuationtechnologies,therearemoreopportunitiestoadvancethevibrationcontrolofmechanicalsystems.Forinstance,byutilizingsensorssuchasaccelerometers,straingauges,anddisplacementsensors,thevibrationbehaviorofmechanicalsystemscanbemonitoredinreal-time,leadingtomoreaccurateandefficientcontrol.Besides,byimplementingactuationtechnologiessuchaspiezoelectricactuators,magnetostrictiveactuators,andshapememoryalloys,thecontrolinputscanbemorepreciseandresponsive,leadingtobettervibrationsuppressionperformance.

Insummary,whiletheoptimization-basedPIDcontrolmethodproposedinthispaperiseffectiveinvibrationcontrolofmechanicalarms,therearealsoothercontrolstrategiesandadvancementsinsensingandactuationtechnologiesthatcanbeexploredandapplied.Continuousresearchanddevelopmentinthisfieldcancontributetotheimprovementofthesafety,efficiency,andreliabilityofvariousmechanicalsystemsFurthermore,itisimportanttoconsidertheapplication-specificrequirementsandconstraintsinimplementingvibrationcontrolstrategies.Mechanicalsystemsindifferentindustriesandenvironmentsmayhaveuniquefactorssuchassize,weight,speed,andprecisionthataffecttheirvibrationalbehaviorandaffecttheselectionandtuningofcontrolparameters.Forexample,intheaerospaceindustry,vibrationcontroliscrucialforensuringthestructuralintegrityandperformanceofaircraft,satellites,andlaunchvehicles.Theharshanddynamicconditionsinspaceandduringlaunchposesignificantchallengesfordesigningandintegratingvibrationcontrolsystems.Similarly,inthemanufacturingindustry,vibrationcontrolisessentialforreducingnoise,improvingproductquality,andprolongingequipmentlifespan.Thetrade-offsbetweencontrolperformance,cost,andenergyconsumptionneedtobecarefullyassessedandoptimizedinordertoachievethedesiredoutcomes.

Anotherareaofresearchthatcanenhancevibrationcontrolistheintegrationofartificialintelligence()techniques.Machinelearningalgorithmscanenableadaptiveandrobustcontrolapproachesthatcanautomaticallyadjustthecontrolparametersbasedonchangingenvironmentalconditionsandoperatingstates.Forexample,reinforcementlearningalgorithmscanlearnoptimalcontrolpoliciesbytrialanderrorinteractionswiththesystem,whileneuralnetworkscanprovidenon-linearmappingbetweeninputandoutputsignals.Theuseofinvibrationcontrolcanalsoenablepredictivemaintenancecapabilitiesbyanalyzingvibrationdataanddetectinganomaliesandfaultsinreal-time.

Overall,vibrationcontrolisacriticalaspectofmechanicalsystemdesignandoperation,andtherearediverseresearchopportunitiesforadvancingthefield.Theoptimization-basedPIDcontrolmethodpresentedinthispaperisavaluablecontributiontotheexistingbodyofknowledgeandcanserveasafoundationforfurtherinvestigationandcomparisonwithotherapproaches.Byleveragingthecomplementarystrengthsofmultiplecontrolmethodsandtechno