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基于改进粒子群算法的PIDNN解耦控制研究(英文) ResearchonImprovedParticleSwarmAlgorithm-basedPIDNNDecouplingControl Abstract: Inrecentyears,therehasbeenanincreasinginterestinthedevelopmentofintelligentcontrolalgorithmsforcomplexsystems.Thispaperproposesanimprovedparticleswarmalgorithm-basedPIDNN(Proportional-Integral-DerivativeNeuralNetwork)decouplingcontrolmethod.TheaimofthisresearchistoaddressthelimitationsoftraditionalPIDcontrolmethodsindealingwithsystemswithstrongcouplingeffects.Byincorporatingneuralnetworksintothecontrolframework,theproposedmethodisabletoadaptivelyadjustcontrolparametersinreal-timetoachievedecouplingcontrol. 1.Introduction Inpracticalcontrolsystems,thepresenceofstrongcouplingeffectscanleadtopoorcontrolperformanceandinstability.TraditionalPIDcontrolmethods,whichrelyonmanualtuningofcontrolparameters,maynotbeabletoeffectivelyhandlesuchcomplexsystems.Thismotivatestheneedfordevelopingintelligentcontrolalgorithmsthatcanautomaticallyadjustcontrolparametersbasedonthesystemdynamics. 2.PIDNNArchitecture TheproposedPIDNNarchitectureconsistsofthreemaincomponents:thePIDcontroller,theneuralnetwork,andthefeedbackloop.ThePIDcontrollercalculatesthecontrolsignalbasedontheerrorbetweenthedesiredoutputandtheactualoutput.Theneuralnetworkcomponentisresponsibleforlearningthenonlinearmappingbetweenthesysteminputsandoutputs,anditcontinuouslyadjustsitselftoimprovecontrolperformance.Thefeedbackloopprovidesthenecessaryinformationforupdatingthecontrolparameters. 3.ImprovedParticleSwarmAlgorithm Theparticleswarmalgorithmisapopulation-basedoptimizationtechniquethatsimulatesthebehaviorofaflockofbirdssearchingforfood.Inthisresearch,animprovedversionoftheparticleswarmalgorithmisusedtooptimizethecontrolparametersofthePIDNN.Thealgorithmincludesseveralmodifications,suchastheintroductionofasearchspaceconstraintandadynamicinertiaweight,toenhancethesearchcapabilityandconvergencespeed. 4.DecouplingControl ThedecouplingcontrolisachievedbytrainingtheneuralnetworkcomponentofthePIDNNtolearntheinversedynamicsof