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JCENTSOUTHUNIV20121927742781DOI101007/S1177101213416INTELLIGENTANTISWINGCONTROLFORBRIDGECRANECHENZHIMEI陈志梅1,MENGWENJUN孟文俊2,ZHANGJINGGANG张井岗11SCHOOLOFELECTRONICINFORMATIONENGINEERING,TAIYUANUNIVERSITYOFSCIENCEANDTECHNOLOGY,TAIYUAN030024,CHINA2SCHOOLOFMECHANICALENGINEERING,TAIYUANUNIVERSITYOFSCIENCEANDTECHNOLOGY,TAIYUAN030024,CHINACENTRALSOUTHUNIVERSITYPRESSANDSPRINGERVERLAGBERLINHEIDELBERG2012ABSTRACTANEWINTELLIGENTANTISWINGCONTROLSCHEME,WHICHCOMBINEDFUZZYNEURALNETWORKFNNANDSLIDINGMODECONTROLSMCWITHPARTICLESWARMOPTIMIZATIONPSO,WASPRESENTEDFORBRIDGECRANETHEOUTPUTSOFTHREEFUZZYNEURALNETWORKSWEREUSEDTOAPPROACHTHEUNCERTAINTIESOFTHEPOSITIONINGSUBSYSTEM,LIFTINGROPESUBSYSTEMANDANTISWINGSUBSYSTEMTHEN,THEPARAMETERSOFTHECONTROLLERWEREOPTIMIZEDWITHPSOTOENABLETHESYSTEMTOHAVEGOODDYNAMICPERFORMANCESDURINGTHEPROCESSOFHIGHSPEEDLOADHOISTINGANDDROPPING,THISMETHODCANNOTONLYREALIZETHEACCURATEPOSITIONOFTHETROLLEYANDELIMINATETHESWAYOFTHELOADINSPITEOFEXISTINGUNCERTAINTIES,ANDTHEMAXIMUMSWINGANGLEISONLY01RAD,BUTALSOCOMPLETELYELIMINATETHECHATTERINGOFCONVENTIONALSLIDINGMODECONTROLANDIMPROVETHEROBUSTNESSOFSYSTEMTHESIMULATIONRESULTSSHOWTHECORRECTNESSANDVALIDITYOFTHISMETHODKEYWORDSBRIDGECRANEANTISWINGCONTROLFUZZYNEURALNETWORKSLIDINGMODECONTROLPARTICLESWARMOPTIMIZATION1INTRODUCTIONBRIDGECRANESAREWIDELYUSEDININDUSTRYFORTRANSPORTINGHEAVYLOADSANDHAZARDOUSMATERIALSINSHIPPINGYARDS,CONSTRUCTIONSITES,STEELMILLS,NUCLEARPOWERANDWASTESTORAGEFACILITIESANDOTHERINDUSTRIALCOMPLEXESTHECRANESHOULDMOVETHELOADASFASTASPOSSIBLEWITHOUTANYEXCESSIVEMOVEMENTATTHEFINALPOSITIONHOWEVER,MOSTOFTHECOMMONBRIDGECRANERESULTSINASWINGMOTIONWHENTHEPAYLOADISSUDDENLYREMOVEDAFTERARAPIDMOTIONTHESWINGMOTIONCANBEREDUCEDFINALLY,BUTWILLBETIMECONSUMING,IE,REDUCINGTHEFACILITYAVAILABILITYASWELLASPRODUCTIVITYTHEFAILUREOFCONTROLLINGCRANEALSOMIGHTCAUSEACCIDENTANDMAYHARMPEOPLEANDSURROUNDINGFORTHISREASON,THEREHASBEENINCREASINGINTERESTINTHEDESIGNOFANANTISWINGCONTROLSCHEMEFORCRANESYSTEM111SLIDINGMODEVARIABLESTRUCTURECONTROLHASATTRACTEDINTENSIVERESEARCHDUETOITSROBUSTNESSTOSYSTEMPARAMETERUNCERTAINTIESANDEXTERNALDISTURBANCESONTHESLIDINGSURFACESOFAR,THETHEORYSYSTEMINTHEFIELDHASBEENBUILTPERFECTLYANDUSEDWIDELYFORPRACTICALSYSTEMS1215SOMERESEARCHERSUSEDTHEVARIABLESTRUCTURECONTROLWITHSLIDINGMODESTOCONTROLTHECRANESYSTEM1415AFUZZYANTISWINGCONTROLSCHEMEWASPROPOSEDFORATHREEDIMENSIONALOVERHEADCRANE1ANANTISWAYANDTRACKINGCONTROLBYUSINGTHEINPUT/OUTPUTLINEARIZATIONAPPROACHFORHARBORMOBILECRANESANDACONSTANTGAINPARTIALSTATEFEEDBACKCONTROLLERFORROTARYCRANESWEREPRESENTED23FORANOVERHEADCRANE,ANORMALPROPORTIONALANDDERIVATIVEPDREGULATORANDAFUZZYCEREBELLARMODELARTICULATIONCONTROLLERCMACWASDESIGNED,ANDTHISCONTROLCANREALIZEBOTHPOSITIONTRACKINGANDANTISWING11,BUTTHESYSTEMATICMODELWASSIMPLIFIEDASTHELINEARMODELINTHESEREFERENCESANADAPTIVESLIDINGMODEFUZZYCONTROLAPPROACHFORATWODIMENSIONALOVERHEADCRANEWASSTUDIED,ANDTHESYSTEMDISPLAYEDHIGHFREQUENCYCHATTERING14MOREOVER,MOSTRESEARCHERSHAVETREATEDTHECRANESYSTEMASASINGLEINPUTSYSTEM,WITHOUTCONSIDERINGTHECHANGESOFLIFTINGROPETHEPARTICLESWARMOPTIMIZATIONPSOALGORITHM,DEVELOPEDBYKENNEDYANDEBERHART,ISANEVOLUTIONARYALGORITHMWHICHISINSPIREDBYTHEMECHANISMOFBIOLOGICALSWARMSOCIALBEHAVIORSUCHASFISHSCHOOLINGANDBIRDFLOCKINGITDIFFERSFROMTHEOTHEREVOLUTIONARYTECHNIQUESINTHEADOPTIONOFVELOCITYOFINDIVIDUALS,ANDITCANSEARCHMORERANDOMLYTHANGENETICALGORITHMGAANDAVOIDFALLINGINTOTHELOCALOPTIMUMWITHFASTERCONVERGENCESPEED1617INORDERTOSOLVETHEABOVEPROBLEMS,AIMINGATTHEFOUNDATIONITEMPROJECT51075289SUPPORTEDBYTHENATIONALNATURALSCIENCEFOUNDATIONOFCHINAPROJECT20122014SUPPORTEDBYTHEDOCTORFOUNDATIONOFTAIYUANUNIVERSITYOFSCIENCEANDTECHNOLOGY,CHINARECEIVEDDATE20110906ACCEPTEDDATE20120417CORRESPONDINGAUTHORCHENZHIMEI,PROFESSOR,PHDTEL863516998245EMAILZHIMEICHEN400163COMJCENTSOUTHUNIV201219277427812775UNCERTAINTIESOFCRANEMODELPARAMETERS,ANEWPSOBASEDFUZZYNEURALNETWORKSLIDINGMODECONTROLFNNSMCMETHODWASPROPOSEDTHENEURALNETWORKWASADOPTEDTOAPPROXIMATETHEUNCERTAINTIESOFSYSTEM,ANDTHEPSOWASUSEDTOOPTIMIZETHEPARAMETERSOFSLIDINGMODECONTROLLER,SOTHECONVERGENCESPEEDOFPARAMETERSISFASTANDITCANQUICKLYREACHTHESLIDINGSURFACEANDIMPROVETHESYSTEMROBUSTNESSCONSIDERINGTHEFRICTIONOFSYSTEM,ITISNEEDLESSTOAPPROXIMATELYDECOUPLEORLINEARIZETHEMODEL,ANDTHECONTROLLERCANACCURATELYPOSITIONTHETROLLEYASWELLASSUPPRESSTHEPAYLOADSWINGEVENINTHEPRESENCEOFPARAMETERSUNCERTAINTIESANDEXTERNALDISTURBANCE2MODELOFBRIDGECRANETHEMODELOFTHEBRIDGECRANESYSTEMISSHOWNINFIG1THETROLLEYANDTHELOADCANBEREGARDEDASPOINTMASS,ANDTHEMOTIONOFLOADWASALWAYSONTHEXYPLANETHEFOLLOWINGDYNAMICEQUATIONOFACRANESYSTEMCANBEOBTAINEDBYUSINGLAGRANGEMETHODASBELOWFIG1SIMPLIFIEDMODELOFCRANE2SINSIN/COSSINSINSIN/SIN2/COSSINSIN/XXLLXXLLLLLXLXXFDXFDMLGLDFFDLMFDLMGLLFDXDLFLM1WHEREX,LANDARETHETROLLEYPOSITION,THELENGTHOFTHESUSPENSIONROPEANDTHESWINGANGLEOFLOAD,RESPECTIVELYMISTHETROLLEYMASSMISTHELOADMASSDXANDDLARETHEVISCOUSDAMPINGCOEFFICIENTSASSOCIATEDWITHTHEXTRAVELINGANDLHOISTINGDOWNMOTIONSRESPECTIVELYFXANDFLARETHEDRIVINGFORCESINTHEXANDLDIRECTIONS,RESPECTIVELYGDENOTESTHEGRAVITATIONALACCELERATIONSUPPOSETHATX1X,X2,XX3L,X4,LX5,X6THEN,THEEQ1CANBEEQUIVALENTTOTHEFOLLOWINGSTATEEQUATION12XX524523425245362255545654656233SIN1SINSINCOSSINSINSIN11SIN2COSLXXLXLXLXDDXXXXXFFMMMMXXDXXXGXXXMXXXDXFFMMMMMXXGXXXXXDXM555554333SINCOSCOSSINCOSLXLXXXXXDXFFMXMXMX23SLIDINGMODECONTROLDEFINETHETRACKINGERRORETAS1DD3DD5DEXXXXELLLLEE3WHEREXD,LDANDDARETHEREFERENCEINPUTTRAJECTORYOFTROLLEYPOSITION,ROPELENGTHANDSWINGANGLE,RESPECTIVELY,INGENERALLY,D0INADDITION,THEREFERENCEINPUTTRAJECTORYXD,LDCANSATISFYTHEASSUMPTIONTHATTHEFIRSTANDSECONDTIMEDERIVATIVESWEREASSUMEDTOBEUNIFORMLYBOUNDEDINTERNALLY,ANDTHATLDISNOTCLOSETOZEROTOAVOIDL0THROUGHOUTTHEENTIRECONTROLTHEN,THESYSTEMERRORMODELISASFOLLOWS1221112344221256633132DDEEEXFGUHUEEELFGUHUEEEFGUHU4WHEREU1FXU2FL1452SINLXDDFXXXMM11GM51SINXHM2252536254SIN1COSSINXLDXFGXXXXDXMMM25252SIN11SINXGXHMMM3F54655524333SIN2COSSINCOSXLGXXXDXXXXDXXXMMJCENTSOUTHUNIV2012192774278127765553333COSCOSSINXXXGHMXMXTHUS,THEBRIDGECRANESYSTEMWASDIVIDEDINTOTHREECOUPLEDSUBSYSTEMSTHEPOSITIONINGSUBSYSTEM,LIFTINGROPESUBSYSTEMANDANTISWINGSUBSYSTEMTHECONTROLOBJECTOFTHEBRIDGECRANEWASTOMOVETHETROLLEYTOITSDESTINATIONANDCOMPLEMENTANTISWINGOFTHELOADATTHESAMETIMEWHENTHESYSTEMMODELEXISTSUNCERTAINTYANDDISTURBANCEFOREXAMPLE,WINDSANDDIFFERENTPAYLOADSINORDERTODECOUPLETHESYSTEM,FOURSLIDINGMODEFUNCTIONSWEREDEFINEDFORTHETHREESUBSYSTEMSWITHSLIDINGSURFACE,SXCXE1E2,SCE5E6,SLCLE3E4,SSXSTAKETHEINDEXREACHINGLAWAS11SGNSSKS522SGNLLLSSKS6WHERECX,CL,C,1,K1,2ANDK2AREPOSITIVENUMBERSTHEN,ACCORDINGTOEQS46,THEFOLLOWINGEQUATIONSCANBEINTRODUCED1211322322XUHFHHFHFHCE314D222D621212SGNSGN/LLLHHCELSKSHXCEHHSKHS23123221GHGHGHGH742D222122SGNLLLCEFLSKSGUUH8HOWEVER,F1,H1,F2,G2,H2,F3,G3,ANDH3AREGENERALLYUNKNOWNINTHEACTUALSYSTEM,THEREFORE,THECONTROLLAWISDIFFICULTTOIMPLEMENT4FUZZYNEURALNETWORK41FUZZYNEURALNETWORKSTRUCTUREASTHEACTUALSYSTEMCONTROLLAWISDIFFICULTTOACHIEVE,THEOUTPUTOFTHREEFUZZYNEURALNETWORKSISUSEDTOAPPROXIMATEF1,H1,F2,G2,H2ANDF3,G3,H3TOREALIZEADAPTIVESLIDINGMODECONTROLFUZZYNEURALNETWORKFNNISDIVIDEDINTOFOURLEVELSINPUTLAYER,MEMBERSHIPLAYER,RULELAYERANDOUTPUTLAYERTHENETWORKSTRUCTUREISSHOWNINFIG2BECAUSEG1G11/MISCONSTANT,THEOUTPUTSOFTHEFIRSTFUZZYNEURALNETWORKARE1FAND1HINTHENETWORK,THEFUZZYSPACEOFEACHVARIABLEISDIVIDEDINTOFIVEFUZZYSETSNM,NS,Z,PS,PMANDTWENTYFIVERULESALTOGETHERASFOLLOWSRLIF11LXAAND12LXATHENLYWHERE12,XEXEARETHESYSTEMINPUTVARIABLES1LAAND2LAAREFUZZYSETSOFX1ANDX2,RESPECTIVELYYISTHESYSTEMATICOUTPUTVARIABLETHEFNNINFERENCEPROCESSISDESCRIBEDASFOLLOWSFIG2FUZZYNEURALNETWORKSTRUCTURE1INPUTLAYEREACHNODEINTHISLAYERISCONNECTEDWITHTHEINPUTVECTORTHEERRORANDITSCHANGERATEARETHENETWORKINPUTS,WHICHISCORRESPONDINGTOTHEJTHNODEINTHEINPUTOUTPUTANDCANBEEXPRESSEDAS1111111111IJJIJIIJJJJWIWXXOFII9WHERE1112,XETXET2MEMBERSHIPLAYERINTHISLAYER,INPUTVARIABLESAREDEFUZZIED,GAUSSIANMEMBERSHIPFUNCTIONISCHOSEN222222222221,EXPIJIJIJIJIJJIJIJJJJJWICIWCOFII10WHERECIJANDIJARERESPECTIVELYREPRESENTEDASTHEITHVARIABLESJTHNODEGAUSSIANFUNCTIONSCENTRALVALUEANDTHESTANDARDDEVIATION,BOTHOFTHEMAREADJUSTABLEPARAMETERS3RULELAYER,THATIS,THEMULTIPLICATIONOPERATIONONFUZZYRULES322333311333311,25IJJIJIIIIJJJJWIWXXJOFII11WHERE3IXISTHEITHINPUTVARIABLEOFTHETHIRDLAYER4OUTPUTLAYER,THATIS,OUTPUTVARIABLECLARITYJCENTSOUTHUNIV20121927742781277724414444JIIJIJJIVXOFII12WHEREVARE4,IJW4,IJV4,IJURESPECTIVELY,WHICHARETHECONNECTIONWEIGHTSBETWEENTHERULEOFTHEFIRSTJANDTHEOUTPUTNODE,ALLOFTHEMAREADJUSTABLEPARAMETERS42WEIGHTADJUSTMENTTHEWEIGHTCHOICEHADATREMENDOUSINFLUENCEONSYSTEMPERFORMANCES,IFTHEWEIGHTISINAPPROPRIATE,THECONVERGENCESPEEDOFNEURALNETWORKSWOULDBEREDUCEDTHISARTICLECARRIEDONTHETRAININGBASEDONTHEGRADIENTDESCENTLAWTOTHENETWORKWEIGHTDEFINEOBJECTIVEFUNCTIONASFOLLOWS44212JJJMDO13WHERE4JDISTHEDESIREDOUTPUTOFTHENETWORK,WHILE4JOISTHEACTUALOUTPUTOFTHENETWORKSUPPOSETHATTHELEARNINGRATEOFV,AIJANDBIJARE1,2AND3,RESPECTIVELY,THEADJUSTMENTVALUESARE1423JIJIJIJIJMVIMAAMBB14WHEREVARE4,IJW4IJVAND4,IJURESPECTIVELYSUPPOSE4,IJW4IJVAND4IJUAS1ASSEENFROMFIG2,THEREVERSETRANSMISSIONERROROFTHEOUTPUTLAYERISDESCRIBEDAS4444444JJJJJJJFIMMIFII44JJDO15AS31,IJWTHERULELAYERHADONLYTHEREVERSETRANSMISSIONERROR3333333JJJJJJJFIMMIFII44KJKKW16THEDEGREEOFMEMBERSHIPLAYERANTIPASSERRORIS2222222JJJJJJJFIMMIFII33KKKO17THEN,THEREVISEDTHREEPARAMETERSOFV,AIJANDBIJAREASFOLLOWS43JJMVOV181222IJJIJJIJIJOAMAAB1912232IJJIJJIJIJOAMBBB20ASTHECONTROLLAWSEQS78CONTAINSGNS,ITMAKESTHESYSTEMTOPRODUCEHIGHFREQUENCYCHATTERINGEASILYTHEREFORE,THESATURATIONFUNCTIONISUSEDINSTEADOFTHESIGNFUNCTIONTOSMOOTHCONTROLSIGNALTHEN,THECONTROLLAWCANBEREWRITTENAS1211322322XUHFHHFHFHCE314D222D621212SS/LLLHHCELATSKSHXCEHHATSKHS23123221GHGHGHGH2142D222122SLLLCEFLATSKSGUUH225PARTICLESWARMOPTIMIZATION51PRINCIPLEOFPARTICLESWARMOPTIMIZATIONASSEENINEQS2122,THECONTROLLERPARAMETERSCX,CL,C,1,K1,2ANDK2HAVEADIRECTIMPACTONTHESYSTEMCONTROLLAWTHEGREATERTHEVALUESOFCX,CL,C,1,K1,2ANDK2,THEFASTERTHESYSTEMAPPROACHEDTHESLIDINGSURFACEHOWEVER,TOOLARGEVALUESWOULDMAKETHECONTROLEXCESSIVEANDMAYCAUSETHESYSTEMCHATTERING,WHICHCANAFFECTTHEDYNAMICPERFORMANCESOFAPPROACHINGPROCESSOTHERWISE,THEVALUESARESMALLER,ALTHOUGHTHESYSTEMCHATTERINGISWEAKENED,THESPEEDTHATTHESYSTEMAPPROACHEDTOTHESLIDINGSURFACEWOULDALSOBECORRESPONDINGLYREDUCEDANDTHETIMETOREACHTHESLIDINGSURFACEWOULDBELONGERINORDERTOMEETTHERAPIDNATUREOFTHESYSTEMANDREDUCETHECHATTERING,THEPARTICLESWARMOPTIMIZATIONALGORITHMISADOPTEDTOOPTIMIZETHEVALUESOFCX,CL,C,1,K1,2ANDK2SIMILARTOOTHEREVOLUTIONARYALGORITHMS,PSOALGORITHMWORKSWELLWITHTHEFITNESSVALUEOFEACHPARTICLEBASEDONTHENOTATIONSOF“GROUP”AND“EVOLUTION”16INTHEORIGINALPSO,THEPOSITIONOFEACHPARTICLEINTHESWARMREPRESENTSAPOSSIBLESOLUTIONTHEPOSITIONANDVELOCITYOFPARTICLEIATITERATIONNAREDENOTEDASXI,DNANDVI,DN,RESPECTIVELYTHENEWVELOCITYATTHENEXTITERATION,VI,DN1,ISCALCULATEDBYUSINGITSCURRENTVELOCITYVI,DN,THEDISTANCEBETWEENTHEPARTICLESBESTPREVIOUSPOSITIONPI,DNANDXI,DN,ASWELLASTHEDISTANCEBETWEENTHEPOSITIONOFTHEBESTPARTICLEINTHESWARMPG,DNANDXI,DN,D,D11,D,D22,D,D1IIIGIVNVNRPNXNRPNXN,DMAX,DMAX,DMIN,DMIN1IF1IFIIVNVVVVNVVV23JCENTSOUTHUNIV201219277427812778WHEREISTHEINERTIAWEIGHT1AND2ARETHEACCELERATIONCONSTANTS,NAMELYCOGNITIVEANDSOCIALPARAMETERS,RESPECTIVELYANDR1ANDR2ARETWORANDOMVALUESINTHERANGEOF0,1THEABOVEDETERMINISTICANDPROBABILISTICPARAMETERSREFLECTTHEEFFECTSOFTHEINDIVIDUALMEMORYANDSWARMINFLUENCEONTHEPARTICLEPOSITIONSTHEPOSITIONOFPARTICLEI,XI,DNISITERATIVELYUPDATEDAS,D,D,D11IIIXNXNVN24THEOPTIMALSOLUTIONSCAN,THUS,BEACQUIREDBYCHOOSINGTHEBESTPARTICLESINADDIMENSIONALSPACE,WHEREDISTHENUMBEROFVARIABLESFROMEQS2324,ITCANBEOBSERVEDTHATTHECOLLECTIVEINTELLIGENCEWASTHEDISTINGUISHINGPROPERTYOFTHEPSOMETHODTHEOPTIMIZATIONPROGRESSFORPARAMETERSCX,CL,C,1,K1,2ANDK2INTHECONTROLLAWISINITIALIZEDWITHAGROUPOFRANDOMPARTICLESNTHROUGHOUTTHEPROCESS,EACHPARTICLEIMONITORSTHREEVALUESITSCURRENTPOSITIONXI,THEBESTPOSITIONINPREVIOUSCYCLESPIANDITSFLYINGVELOCITYVITHEOPERATORPLAYEDTHEROLEOFBALANCINGTHEGLOBALSEARCHANDTHELOCALSEARCHINORDERTOIMPROVETHECONVERGENCEPERFORMANCEOFPSOALGORITHMTOASSURETHEINITIALGLOBALSEARCHANDTHESUBSEQUENTLOCALRESEARCH,ATIMEVARYINGINERTIAWEIGHTNISFORMULATED,WHICHISTHEFUNCTIONOFITERATIONNMAX1205NNG25WHEREGMAXISTHEMAXIMUMCALCULATEDCUTOFFGENERATION52DESIGNSTEPSSTEP1INITIALIZEAGROUPOFRANDOMPARTICLESEGGROUPSIZEN,RANDOMPOSITION,VELOCITY,ANDINITIALVECTORSTEP2EVALUATETHEFITNESSVALUEOFEACHPARTICLEACCORDINGTOTHEOBJECTIONFUNCTIONJANDFITNESSFUNCTIONFASFOLLOWST1MIN2JEE26FIT1FJ27STEP3FOREACHPARTICLE,BYCOMPARINGTHEINDIVIDUALFITNESSVALUEATPRESENTANDTHEBESTPOSITIONPBESTITSELFINTHEPAST,THEBESTPOSITIONPBESTISUPDATEDIFTHEPRESENTVALUEISBETTERTHANTHEPASTSTEP4FOREACHPARTICLE,BYCOMPARINGTHEINDIVIDUALFITNESSVALUEANDTHEBESTPOSITIONGBESTOFTHEGROUPWITHTHOSEINTHEPAST,THEGLOBALBESTPOSITIONGBESTISUPDATEDIFTHEPRESENTVALUEISBETTERTHANTHEGLOBALOPTIMALPOSITIONSTEP5UPDATETHEVELOCITYANDPOSITIONOFPARTICLESACCORDINGTOEQS2324STEP6RETURNTOSTEP2IFTHETERMINATIONCONDITIONISNOTMETTHETERMINATIONCONDITIONISGENERALLYTHEPERFECTFITNESSORTHEMAXIMUMCALCULATEDCUTOFFGENERATION6SIMULATIONRESEARCHTOVERIFYTHEEFFECTIVENESSOFTHEPROPOSEDMETHODFNNSMC,ABRIDGECRANESYSTEM10ISINTRODUCEDINTOTHESIMULATION,M1KG,M025KG,DX015N/SM,DL01N/SM,G98M/S2THEDESIREDPOSITIONOFTROLLEYIS07M,LDISTYPEFUNCTION,ASSHOWNINFIG9,THELIFTINGROPELENGTHFROM07MTO04MTO07MTHEINITIALWEIGHTSOFTHREERBFNETWORKSARE0001,THECENTRALVALUESANDWIDTHSOFTWELVERBFNEURONSINHIDDENLAYERARETAKENASFOLLOWS110101010,10101010CT128282828,B295959595,95959595CT236363636,B310101010,10101010CT33333BACCORDINGTOTHEABOVEMETHOD,THESIMULATIONRESULTSARESHOWNINFIGS313FIGURES35DESCRIBETHEOUTPUTOFTHEFUZZYNEURALNETWORK,FIG6DESCRIBESTHEPROGRESSOFPARAMETEROPTIMIZATIONWITHTHEPSO,WHERECX26581,CL02188,C12658,38245,108360,207549,K123672,ANDK226224FIGURES79DESCRIBETHECHANGEOFSYSTEMVARIABLESUSINGFNNSMCANDSMC,THEMAXIMUMSWINGANGLEOFFNNSMCIS01RAD,THEMAXIMUMSWINGANGLEOFSMCIS012RAD,THERAPIDITYOFFNNSMCTHANSMCFIGURES1013DESCRIBETHECHANGECURVESWITH05STEPDISTURBANCEWITHINTHE13SITCANBESEENFROMFIGS1013,THEANTISWINGCAPABILITYOFTHISMETHODISSTRONGERTHANCONVENTIONALSLIDINGMODECONTROLANDTHEMETHODOFREF10INTHEPRESENCEOFDISTURBANCES,THEMAXIMUMSWINGANGLEOFITISONLY01RAD,BUTTHEMAXIMUMSWINGANGLE013RADOFSMCANDIS025RADINREF10ASCANBESEENFROMTHESIMULATIONRESULTS,THEPSOCANSEARCHTHEMOSTEXCELLENTVALUEFASTINTHESOLUTIONSPACETHETHREEGENERATIONSTHESIMULATIONSHOWSTHATTHEPROPOSEDCONTROLMETHODGUARANTEESANTISWINGCONTROLANDACCURATETRACKINGCONTROLOFTROLLEYWHENTHESYSTEMMODELEXISTSUNCERTAINTIESANDTHESLIDINGFUNCTIONCANREACHRAPIDLYTOTHESLIDINGMODESURFACE,WHICHIMPROVESTHESYSTEMROBUSTNESSJCENTSOUTHUNIV201219277427812779FIG3OUTPUTOFFIRSTFUZZYNEURALNETWORKA1FB1HFIG4OUTPUTOFSECONDFUZZYNEURALNETWORKA222CBHGFFIG5OUTPUTOFTHIRDFUZZYNEURALNETWORKA3FB3GC3HJCENTSOUTHUNIV201219277427812780FIG6OPTIMIZATIONPARAMETERSWITHPSOFIG7TROLLEYPOSITIONTRACKINGCURVEFIG8SWINGANGLETRACKINGCURVEFIG9ROPELENGTHTRACKINGCURVEFIG10TROLLEYPOSITIONCURVEWITHPOSITIONINGDISTURBANCEFIG11LOADANGLECURVEWITHPOSITIONINGDISTURBANCEFIG12TROLLEYPOSITIONCURVEWITHSWINGINGDISTURBANCEFIG13LOADANGLECURVEWITHSWINGINGDISTURBANCEJCENTSOUTHUNIV2012192774278127817CONCLUSIONS1ANEWINTELLIGENTANTISWINGCONTROLSCHEMEISPROPOSEDWITHCOMBINATIONOFSMCSROBUSTNESSANDFNNSINDEPENDENCEONSYSTEMMODELTHEBRIDGECRANEISSIMPLIFIEDINTOTHREEMULTIINPUTSUBSYSTEMS,FOURSLIDINGMODESURFACESAREDEFINEDANDFUZZYNEURALNETWORKSSLIDINGCONTROLLERISDESIGNEDITISCAPABLEOFTACKLINGNONLINEARSYSTEMWITHPARAMETERUNCERTAINTIES2COMPAREDWITHCONVENTIONALSLIDINGMODECONTROL,THESYSTEMACHIEVESGOODPOSITIONINGACCURACYANDSIGNIFICANTSWAYREDUCTIONWITHCONSIDERINGCHANGESOFLIFTINGROPEWHENTHEBRIDGECRANESYSTEMMODELHASUNCERTAINTIESANDDISTURBANCE3MOREOVER,BYTHEPSOALGORITHM,THEPARAMETERSOFCONTROLLERAREOPTIMIZEDTOACCELERATESYSTEMCONVERGENCE,THEINHERENTCHATTERINGPHENOMENAOFSLIDINGMODECONTROLCANBEELIMINATEDANDTHEPERFORMANCESOFCONTROLSYSTEMCANBEAMELIORATEDTHESIMULATIONRESULTSSHOWTHATTHECORRECTNESSANDVALIDITYOFTHISMETHODREFERENCES1CHOSK,LEEHHAFUZZYLOGICANTISWINGCONTROLLERFORTHREEDIMENSIONALOVERHEADCRANESJISATRANSACTIONS,2002,4122352432JORGN,ECKHARDA,KLAUSS,OLIVERSTRACKINGANDANTISWAYCONTROLFORBOOMCRANESJCONTROLENGINEERINGPRACTICE,2010,18131443CHANGCHENGYUAN,CHIANGKUOHUNGFUZZYPROJECTIONCONTROLLAWANDITSAPPLICATIONTOTHEOVERHEADCRANEJMECHATRONICS,2008IZUMOTOI,CHENTONGWEN,OHDAIRASADAPTIVEOUTPUTFEEDBACKCONTROLOFGENERALMIMOSYSTEMSUSINGMULTIRATESAMPLINGANDITSAPPLICATIONTOACARTCRANESYSTEMJAUTOMATICA,2007,4312207720855SORENSENKL,SINGHOSEW,DICKERSONSACONTROLLERENABLINGPRECISEPOSITIONINGANDSWAYREDUCTIONINBRIDGEANDGANTRYCRANESJCONTROLENGINEERINGPRACTICE,2007,1578258376YANGJUNGHUA,YANGKUANGSHINEADAPTIVECOUPLINGCONTROLFOROVERHEADCRANESYSTEMSJMECHATRONICS,2007,172/314315