Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach 有用

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1、1012IEEETRANSACTIONSONFUZZYSYSTEMS,VOL.20,NO.6,DECEMBER2012AdaptiveFuzzyControlofaClassofNonlinearSystemsbyFuzzyApproximationApproachBingChen,XiaopingP.Liu,ShuzhiSamGe,Fellow,IEEE,andChongLinAbstractControllingnonstrict-feedbacknonlinearsystemsiscontrollerwasdesignedforsingle-input-single-output(SIS

2、O)achallengingproblemincontroltheory.Inthispaper,wecon-nonlinearstrict-feedbacksystemsbybacksteppingtechnique.sideradaptivefuzzycontrolforaclassofnonlinearsystemswithThebackstepping-baseddesigntechniquewasextendedtonon-nonstrict-feedbackstructurebyusingfuzzylogicsystems.Avari-lineardelayedsystemswit

3、hstrict-feedbackstructurein[8].ableseparationapproachisdevelopedtoovercomethedifficultyfromthenonstrict-feedbackstructure.Furthermore,basedonAlternatively,inrecentyears,intensiveresearchhasbeenfuzzyapproximationandbacksteppingtechniques,astatefeed-carriedoutonapproximation-basedadaptiveneuralnetworkb

4、ackadaptivefuzzytrackingcontrollerisproposed,whichguar-controloradaptivefuzzycontrolofnonlinearsystemswithanteesthatallofthesignalsintheclosed-loopsystemarebounded,lowertriangleformviathebacksteppingtechnique,forinstance,whilethetrackingerrorconvergestoasmallneighborhoodofthesee[5],[7],[11],[20],[29

5、],[33],[34],[41],and[48]foradap-origin.Simulationstudiesareincludedtodemonstratetheeffec-tivenessofourresults.tiveneuralcontroland[1]–[3],[13],[19],[31],[36]–[39],[45],and[49]andreferencesthereinforadaptivefuzzycontrol.Dif-IndexTermsAdaptivefuzzycontrol,fuzzylogicsystems,ferentfromclassicaladaptiveb

6、acksteppingcontrol,inthecasesnonstrict-feedbacksystems.ofadaptiveneuralcontroloradaptivefuzzycontrol,nonlinearsystemfunctionsareunknown,andneuralnetworksorfuzzylogicsystemsareutilizedtomodeltheunknownnonlineari-I.INTRODUCTIONties[24],[25].Furthermore,adaptiveandbacksteppingtech-VERthepastfewyears,th

7、ebacksteppingmethod[15],niquesarecombinedtoconstructstablecontrollers.InrecentO[16],[27]hasbecomeoneofthemostpopularcontrolap-years,theapproximation-basedadaptiveneuralnetworkcontrolproachesforaclasso