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机械臂轨迹跟踪控制——基于EC-RBF神经网络的机械臂模型参考自适应控制.docx

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机械臂轨迹跟踪控制——基于EC-RBF神经网络的机械臂模型参考自适应控制 Abstract: ThispaperproposesamodelreferenceadaptivecontrolmethodbasedonEC-RBFneuralnetworkfortrajectorytrackingcontrolofaroboticarm.Theaimofthisresearchistoachieveaccurateandrobustcontroloftheroboticarm'smotion,allowingittofollowdesiredtrajectorieseffectively.TheEC-RBFneuralnetworkisusedtomodelthedynamicsoftheroboticarm,andanadaptivecontrolalgorithmbasedontheLyapunovtheoryisdesignedtotrackthedesiredtrajectories.Simulationresultsshowthattheproposedcontrolmethodhasgoodperformanceintermsoftrajectorytrackingaccuracyandrobustness. Keywords:roboticarm,trajectorytrackingcontrol,EC-RBFneuralnetwork,adaptivecontrol,Lyapunovtheory 1.Introduction Roboticarmsarewidelyusedinvariousapplicationssuchasindustrialautomation,medicalsurgery,andspaceexploration.Precisemotioncontrolofroboticarmsisofgreatimportanceforachievingdesiredtasks.Trajectorytrackingcontrol,whichreferstotheabilityofaroboticarmtoaccuratelyfollowagiventrajectory,isakeyaspectofroboticarmcontrol. Traditionaltrajectorytrackingcontrolmethodsforroboticarmsusuallyrelyonaccuratemathematicalmodelsofthearmdynamics.However,thesemodelsareoftendifficulttoobtainduetothecomplexandnonlinearnatureoftheroboticarmsystem.Moreover,theymaybeaffectedbyexternaldisturbancesanduncertainties.Therefore,itischallengingtodesigncontrolalgorithmsthatcanachieveaccurateandrobusttrajectorytrackingcontrolforroboticarms. Inrecentyears,neuralnetwork-basedcontrolmethodshavegainedpopularityinthefieldofroboticsduetotheirabilitytoapproximatecomplexnonlinearfunctions.Amongvariousneuralnetworkstructures,RadialBasisFunction(RBF)networkshavebeenwidelyusedformodelingdynamicsystems.However,traditionalRBFneuralnetworkshavelimitationsintermsofaccuracyandgeneralizationability.Toaddresstheselimitations,anEvolutionaryComputation(EC)-basedapproachcanbeintegratedwithRBFnetworkstoenhancetheirperformance. 2.EC-RBFNeuralNetworkModel TheEC-RBFneuralnetworkisacombinationofRBFnetworksandevolutionarycomputationtechniques.TheRBFnetworkisuseda