Adaptive Backstepping Control for a Class of Nonaffine Nonlinear Systems Based Neural Networks

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1、SecondInternationalSymposiumonIntelligentInformationTechnologyApplicationAdaptiveBacksteppingControlforaClassofNonaffineNonlinearSystemsBasedNeuralNetworksJianqingMin,ZibinXu,YingguoFangCollegeofBiologyandEnvironmentEngineering,ZhejiangShurenUniversity,Hangzhou,

2、ZheJiang,310015,Chinaminjq@sina.com,hzxuzibin@gmail.comAbstractInthispaper,theproblemofdesigninganadaptiveneuralnetworkscontrollerisstudiedaimingataclassofAimingataclassofnonaffinenonlinearsystemwithnonaffinenonlinearsystemwithuncertainties,andauncertainties,ana

3、daptivebacksteppingneuralcontrollersimulationexampleispresentedtodemonstratethedesignispresented.Byapplyingbacksteppingdesigneffectivenessoftheproposedcontroldesign.strategyandonlineapproachingnonlinearitywithfullytunedradialbasisfunction(RBF)neuralnetworks,the2

4、.ProblemformulationadaptivetuningrulesarederivedfromtheLyapunovstabilitytheory.AnonlineartrackingdifferentiatorisConsidertheuncertainnonaffinenonlinearsysteminintroducedtodealwiththeproblemofextremelytheformofexpandedoperationquantityofbacksteppingmethod.Thedeve

5、lopedcontrolschemeguaranteesthatallthesignals⎧x&i=fi(Xi)+gi(Xi)xi+1(1≤i

6、i=[x1,x2L,xi]∈R;u∈Risthecontrolinput;fi(Xi)1.Introductionandgi(Xi)areunknownsmoothfunctions,andcannotbeexpressedaslinearizationform.ThestudyonuncertainnonlinearsystemsadaptiveTheaimistodesignacontrollerthatcaneliminatethecontrolhasattractedwideattentionandsomeim

7、portanteffectofunexpectedfactors,sothatthesystemoutputcanachievementswereobtainedduringtherecentyears[1-6],trackthedesiredcontroloutputanditcanbeensuredallinparticular,thenonlinearsystemscontrolbasedonthesignalsoftheclosed-loopsystemareuniformlyneuralnetworksiso

8、neoftheactiveresearchareas.ultimatelybounded.However,themajorityofresearchresultsfocusonaffineBeforethemainresultsaregiven,theassumptionsandsystemsratherthannonaffine