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基于多目标优化的非线性模型预测控制的研究（英文）.docx

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基于多目标优化的非线性模型预测控制的研究（英文） ResearchonNonlinearModelPredictiveControlBasedonMulti-ObjectiveOptimization Abstract: NonlinearModelPredictiveControl(NMPC)isapopularcontrolstrategythatcanhandlenonlinearandconstrainedsystemseffectively.Inrecentyears,researchershavefocusedonimprovingtheperformanceofNMPCbyincorporatingmulti-objectiveoptimizationtechniques.Thispaperpresentsareviewoftheresearchonmulti-objectiveoptimization-basedNMPC,highlightingitsadvantages,limitations,andpotentialforfuturedevelopment. 1.Introduction NMPCisafeedbackcontrolstrategythatsolvesanoptimizationproblemateachtimesteptofindtheoptimalcontroltrajectory.Byconsideringsystemdynamics,constraints,anddesiredobjectives,NMPCcaneffectivelyhandlenonlinearsystems.However,NMPCistypicallyassociatedwithasingle-objectiveoptimizationproblem,neglectingthetrade-offsbetweencompetingobjectives.Multi-objectiveoptimizationaimstosimultaneouslyoptimizemultipleobjectives,resultinginasetoffeasiblesolutionscalledtheParetofront.Incorporatingmulti-objectiveoptimizationintoNMPCcanprovideaflexibleandrobustcontrolstrategy. 2.Multi-objectiveOptimizationforNMPC Theintegrationofmulti-objectiveoptimizationtechniqueswithNMPCoffersseveraladvantages.Firstly,itallowsfortheconsiderationofconflictingobjectives,suchascontrolperformanceandenergyconsumption,whicharedifficulttohandleinasingle-objectiveoptimizationframework.Secondly,itprovidesasetoftrade-offsolutionsthatcanbeexploredbydecision-makerstoselectthebestcompromisesolution.Additionally,theParetofrontgeneratedbymulti-objectiveoptimizationoffersinsightsintosystembehaviorandtheimpactofdifferentcontroldecisionsonmultipleobjectives. 3.ApplicationsofMulti-objectiveOptimization-basedNMPC Multi-objectiveoptimization-basedNMPChasbeenappliedtovariouscontrolproblems,includingrobotics,chemicalprocesses,andrenewableenergysystems.Forinstance,inroboticcontrol,multi-objectiveoptimizationcanbalancethetrajectorytrackingaccuracyandenergyconsumption.Inchemicalprocesses,multi-objectiveoptimizationhelpsinmaximizi