車輛工程外文翻譯-汽車主動懸架系統的神經網絡控制運算法則的研究【中文2904字】【PDF+中文W
車輛工程外文翻譯-汽車主動懸架系統的神經網絡控制運算法則的研究【中文2904字】【PDF+中文W,中文2904字,車輛,工程,外文,翻譯,汽車,主動,懸架,系統,神經網絡,控制,運算,法則,研究,中文,2904,PDF
外文翻譯
外文資料名稱：汽車主動懸架系統的神經網絡控
制運算法則的研究
外文資料出處：International Conference on Neural
Networks and Brain, 2005.
【中文2904字】
汽車主動懸架系統的神經網絡控制運算法則的研究
L.J.Fu, J.G.Cao 重慶工學院車輛工程系
中國重慶市楊家坪興盛路4號，400050
C. R. Liao, B. Chen 重慶技術學院車輛工程系
中國重慶市楊家坪興盛路4號，400050
E-mail: flj@cqit.edu.cn
周祥 譯
摘要:為適應不同路面狀況和汽車運行狀況,半可控懸架由從動彈簧和活動減振器組成。由于主動懸架結構復雜并且消極懸架無法滿足各種路面條件和汽車運行狀態的要求,因此半可控懸架系統是目前最常用的懸架系統。本文將著重介紹自適應神經控制的汽車懸架循環神經網絡模擬控制器。懸架系統神經網絡不同于汽車懸架的動態參數,并且還能夠為神經自動調節控制器提供學習信號,為了檢驗控制結果，在DSP微處理系統基礎上為中巴安裝液壓減振器和多維控制系統,并在各種速度和路面上進行實驗.將此控制結果和開環消極懸架系統進行比較,結果表明神經網絡控制運算在減少微型客車振動方面表現的非常良好。
1．概述
汽車懸架系統的主要功用是支撐車身的重量,并且使汽車穩定有效的進行轉向操縱控制,同時有效的分離路面波動對車身的影響。不同的需要導致設計的要求不同,半自動懸架由從動彈簧和需要克服不同路面狀況和汽車運行條件的阻尼離的自動減振器組成。由于主動懸架結構復雜而傳統的消極式懸架無法滿足不同路面狀況和汽車運行狀況的要求。因此,半自動懸架是目前最常用的懸架系統。半自動懸架系統的優點是帶有液壓減振使車身在低動力情況下振動降低。目前,許多控制系統是為半自動懸架系統而開發的。從Karnoopp的Skyhook方法開始。這個方法主要是使緩沖器承受一定的力的作用,而這個力是與汽車全速時懸架上的質量成一定比例的。許多調查都是用一維模型,它可以推導出模糊的控制點和控制運算法則。如LQG和活躍控制[2,3]。由于汽車懸架固有非線性特性,導致這種控制方法不能充分發揮半自動懸架的功用。為充分利用懸架系統的非線性功用。如模糊邏輯控制。神經網絡控制和模糊神經控制等智能化控制方法近來都已被科研人員用于非線性懸架系統控制[4,5]。
本文，一種神經自適應控制控制器被用于控制汽車懸架神經網絡和瞬邊的MR減振器的循環振動?？刂破鞯慕Y構設計和控制運算法則將在第2部分進行詳細敘述。懸架的循環神經網絡動態模擬在第3部分進行介紹控制系統實驗在第4部分，第5部分是總結。
1. 汽車懸架的多維自調節控制法則
神經模糊控制系統將在本文進行介紹，由圖1可知，它是由模糊神經網絡和神經網絡模型構成的微型客車懸架。神經網絡模糊控制即自適應控制，它有學習和控制的功能。它的循環神經網絡功用是用來鑒別中巴車懸架的模擬參數。圖1中的y(t)和yd(t)分別是系統實際輸出和系統理想輸出。xl(t)是系統實際輸出和理想輸出之間的誤差。x2(t)是系統實際輸出和理想輸出的誤差率xl(t)和x2(t)定義如下：
xI (t) e(t)= y(t)- Yd (t) (1)
X2 (t)= e(t)= e(t + 1)- e(t) (2)
圖1.懸架神經網絡控制系統的結構
網絡控制系統：整體集的定義分別如下： = [- E,E], = [- E,E], =[-U,U].神經模糊控制器有四層神經元。第一層和第二層和與模糊法則相一致。第三層與推理相一致，而第四層與模糊法則相一致。, 和的集合分別分成7個子集，，，集的組成分別如下：
X1 = {NB, NM, NS, ZE, PS, PM, PB}
X2 = {NB, NM, NS, ZE, PS, PM, PB}
U = {NB, NM, NS, ZE, PS, PM, PB}
本文，將用高斯函數解決模糊集,和模糊集的組成，其函數的第一如下：
圖2.自動懸架神經網絡控制器簡圖
，由圖2可知，輸入/輸出如下：1：
和
和
都是神經網絡的輸入部分。是其重量，是其輸出部分，，都是高斯函數的重要值。神經網絡控制器的學習法則是以斜率誤差信號逆向傳遞方法為基礎的。誤差逆向傳遞方法通過使函數[5]損失降至最低自動調節重量。
3．懸架循環神經網絡動態模擬法則
懸架神經網絡設計用于將實際輸出量通過第三層神經網近似反饋給潛在的循環層，結構如圖3所示。其性能是使循環神經網絡能夠自動獲知周圍環境并且據此提高其重量自動適應作用.循環神經網絡輸入信號和和潛在層的邏輯反饋循環神經的輸出量的總輸出量對等于神經。
圖3.懸架系統神經網絡模擬簡圖。
是循環神經網絡的負荷，是潛在層邏輯循環反饋神經的輸出神經量，分別是輸入神經量和反饋神經量。激活函數是輸入函數和輸出函數的線性函數，潛在層神經的激活是S形的函數。
它的反函數通過誤差信號定義如下：
是誤差能量的瞬時值.神經元的突出質量一步一步連續的自動調節直至系統達到 穩定狀態，即突出質量基本上穩定。
從式1，2和3可知：
從4，5和6分析和分別推導出循環分子式。
突出質量可以由下式計算得到：
· 是速率參數，詳細分析循環算法獲得速率參數值是相當復雜的。根據式13得，循環神經網絡質量矢量能夠自動調節。函數如下，其變值經過t時間可以定義為：
我們通過式13和式14可以知道誤差信號如下：
函數增量經過t時間可以定義為：
.
4 .路面測試結果分析
神經控制運算的正確性的證明，帶有MR液壓減振器的微型客車懸架在中國已經大量投產制造. 微型客車自適應懸架系統由一個DSP微處理系統，8個加速度傳感器，4個MR液壓減振器和一個輸入電壓為12v的可控循環電流控制器組成.DSP微處理器通過傳感器獲取懸架彈簧負載和空載時候的懸架振動信號.根據振動信號和本文的控制圖，DSP微處理系統通過調節控制信號來調節MR液壓減振器中的電磁線圈的電流。 MR液壓減振器電磁線圈產生的磁場能夠在壓縮沖程和反彈過程中調節MR液壓減振器中流體運行狀態。
本文描述的是以神經網絡控制為基礎的微型客車懸架的路面測試，其速度分別為30，40，50 km/h.路面測試過程中微型客車以恒定的速度運行。自適應懸架分別以神經網絡和消極懸架系統在同樣的路面和運行速度下進行測試實驗。表1的測試結果表明神經網絡控制自適應懸架能夠在懸架彈簧重載和空載的條件下都能減小振動。
圖4描述的是滿載和空載時候的消極和自適應微型客車懸架在D級路面上的振動曲線圖。很明顯神經網絡控制主要提高減緩振動的能力。受力曲線圖表明自適應懸架系統和消極懸架系統相比較能夠明顯減小微型客車的振動。減振器有卓越的模糊控制原理和模擬推理，帶有神經網絡控制的自適應懸架系統遠乘舒適性能和路面穩定保持性能。
表1 微型客車懸架路面測試結果
微型客車懸架滿載和空栽時速度變換曲線（D級路況）
圖4.微型客車振動力曲線圖 （左）滿載 （右）空載 （速度40km/h）
結論
本文中主要講述的是微型客車的一種新型的循環神經網絡模型和模糊神經控制原理.根據要求使用8個加速度傳感器和一個信號處理器?？紤]到MR減振器的復雜性，動態參數載入硬盤進行仿真.它表明自適應控制系統可以通過模糊神經控制和循環神經網絡懸架達到完全控制作用。由于控制法設計，增益調度策略和硬件循環仿真的開發本文限于微型客車的具體參數，在懸架參數變化的情況下此方法可以延伸到其它半主動懸架系統.路面實驗結果表明模糊神經控制可以有效改善微型客車行使的舒適性和穩定性。使用DSP控制器能有效的減小整個車身的振動，包括滿載時候和非滿載時候的振動。模糊神經控制器可以減少對對控制系統性能影響很大的模擬參數的變化。
參考文獻
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StudyonNeuralNetworksControlAlgorithmsforAutomotiveAdaptiveSuspensionSystemsL.J.Fu,J.G.CaoSchoolofAutomobileEngineering,ChongqingInstituteofTechnology,XingshengRoadNo.04Yangjiaping,Chongqing,China400050E-mail:Abstract-Thesemi-activesuspension,whichconsistsofpassivespringandactiveshockabsorberinthelightofdifferentroadconditionsandautomobilerunningconditions,isthemostpopularautomotivesuspensionbecauseactivesuspensioniscomplicatedinstructureandpassivesuspensioncannotmeetthedemandsofvariousroadconditionsandautomobilerunningconditions.Inthispaper,aneurofuzzyadaptivecontrolcontrollerviamodelingofrecurrentneuralnetworksofautomotivesuspensionispresented.Themodelingofneuralnetworkshasidentifiedautomotivesuspensiondynamicparametersandprovidedlearningsignalstoneurofuzzyadaptivecontrolcontroller.Inordertoverifycontrolresults,amini-busfittedwithmagnetorheologicalfluidshockabsorberandneurofuzzycontrolsystembasedonDSPmicroprocessorhasbeenexperimentedwithvariousvelocityandroadsurfaces.Thecontrolresultshavebeencomparedwiththoseofopenlooppassivesuspensionsystem.Theseresultsshowthatneuralcontrolalgorithmexhibitsgoodperformancetoreductionofmini-busvibration.I.INTRODUCTIONThemainfunctionsofautomotivesuspensionsystemaretoprovidesupporttheweightofautomobile,toprovidestabilityanddirectioncontrolduringhandlingmaneuversandtoprovideeffectiveisolationfromroaddisturbances.Thesedifferenttasksleadtoconflictingdesignrequirements.Thesemi-activesuspension,whichconsistsofpassivespringandactiveshockabsorberwithcontrollabledampingforceinthelightofdifferentroadconditionsandautomobilerunningconditions,isthemostpopularautomotivesuspensionbecausetheactivesuspensioniscomplicatedinstructureandconventionalpassivesuspensioncannotmeetthedemandsofdifferentroadconditionsandautomobilerunningconditions.Simi-activesuspensionwithvariablemagnetorheological(MR)fluidshockabsorbershassomeadvantagesinreducingautomobilevibrationatrelativelowcastandpower.Sofar,thereareanumberofcontrolmethodsthathavebeendevelopedforsemi-activesuspension,startwithskyhookmethoddescribedbyKarnoopp,etal.lThismethodattemptstomaketheshockabsorberexertaforcethatisproportionaltotheabsolutevelocitybetweensprungmasses.SomeinvestigationsuseC.R.Liao,B.ChenSchoolofAutomobileEngineering,ChongqingInstituteofTechnology,XingshengRoadNo.04Yangjiaping,Chongqing,China400050E-mail:chenbao(linearsuspensionmodel,whichislinearizedaroundtheoperationalpoints,andcontrolalgorithmarederivedusinglinearmodels,suchasLQGandrobustcontrol2,3.Thesecontrolmethodscannotmakeafullexploitationofsemi-activesuspensionresourcesbecauseofautomotivesuspensionisinherentnon-linearperformance.Inordertoimproveperformanceofnonlinearsuspensionsystem,someintelligentcontroltechniques,suchasfuzzylogiccontrol,neuralnetworkscontrolandneurofuzzycontrol,havebeenrecentlyappliedtononlinearsuspensioncontrolbyresearchers4,5.Inthispaper,aneurofuzzyadaptivecontrolcontrollerisappliedtocontrolsuspensionvibrationviamodelingofrecurrentneuralnetworksofautomotivesuspensionandcontinuouslyvariableMRshockabsorbers.Thecontrollerstructuresdesignandneurofuzzycontrolalgorithmsarepresentedinsection2.Arecurrentneuralnetworksdynamicsmodelingofsuspensionareshownrespectivelyinsection3.Thecontrolsystemexperimentationsaregiveninsection4andsomeconclusionsarefinallydrawninsection5.HI.NEUROFUZZYADAPTIVECONTROLALGORITHMSFORAUTOMOTIVESUSPENSIONSTheneurofuzzycontrolsystempresentedinthispaper,showninFigure1,iscomposedofaneurofuzzynetworkandarecurrentneuralnetworkmodelingofmini-bussuspension.Theneurofuzzynetworkisdefinedasadaptivecontroller,whichhasfunctionoflearningandcontrol.Thefunctionofrecurrentneuralnetworkistoidentifymini-bussuspensionmodelparameters.y(t)andyd(t)aresystemactualoutputandsystemdesireoutputrespectivelyinFigure1.xl(t)issystemerrorofsystemactualoutputbetweensystemdesireoutput,x2(t)issystemerrorrateofsystemactualoutputbetweensystemdesireoutput.xi(t)andx2(t)aredefinedasfellows:xI(t)e(t)=y(t)-Yd(t)(1)X2(t)=e(t)=e(t+1)-e(t)(2)0-7803-9422-4/05/$20.00C2005IEEE1795Fig.1.structureofneuralnetworkscontrolsystemforsuspensionnetworkscontrolsystem.Theglobalsetsoflinguisticvariablesaredefinedrespectivelyasfellows:-=-E,E,1=-AtJuU-U,U.Theneurofuzzycontrollerhasfourlayersne-urons,inwhichthefirstandthesecondlayerscorrespondtothefuizzyrulesif-part,thethirdlayercorrespondstotheinferenceandtheforthlayercorrespondstothefuzzyrulesthen-part.Thesetsxl,x2anduarerespectivelydivinedintosevenfuzzysubsetsofwhichfuzzysetsX1,X2Uarecomposedasfallowsrules:X1=NB,NM,NS,ZE,PS,PM,PBX2=NB,NM,NS,ZE,PS,PM,PBU=NB,NM,NS,ZE,PS,PM,PBInthispaper,theGaussianmembershipfunctionareusedinelementsoffuzzysetsX1X2andtheelementsoffuzzysetUisdefinedasfollowingmembershipfunctionci(u)J0(otherwise)0(3)=I(3)k=1,2,3.49j=13,23,3.74949Layer4:(4)-(3)wkand0(4)=I(4)/0(3)k=1k=1Wherexl(t)x2(t)aretheinputsofneuralnetworks,wkisweightofneuralnetwork,0(4)iStheoutputofneuralnetworksinwhich0(4)=U,ai,b,jarethecentralvaluesofGaussianmembershipfunction.Learningalgorithmsoftheneuralnetworkscontrollerisbasedongradientdescentbymeansoferrorsignalback-propagationmethod.Theerrorback-propagationalgorithm.saccomplishsynapticweightadjustmentthroughminimizationofcostfunction5.m.ALGORITHMFORRECURRENTNEURALNETWORKSSUSPENSIONDYNAMICALMODELINGArecurrentneuralnetworkdesignedtoapproximatetotheactualoutputofsuspensiony(t)isthree-layerneuralnetworkwithonelocalfeedbackloopinthehiddenlayer,whosearchitecturesareshowninFigure3.Thepropertythatisofprimarysignificanceforrecurrentneuralnetworkistheabilityofthenetworktolearnfromitsenvironmentandtoimproveitsperformancesbymeansofprocessofadjustmentsappliedtoitsweights.TherecurrentnetworkwithinputsignalII(t)=u(t)andI2(t)=y(t-1)hasoutputy(t)bylocalfeedbackloopneuroninthehiddenlayerwhoseoutputsumisSj(t)correspondingtotheneuronjth.(3)Fig.2.schematicofneuralnetworkscontrollerforadaptivesuspensionWhereU*Eu.Theinput/outputispresentedasfollowsaccordingtoFigure2.Layer1:I(1)x(t)andO)xi(t)i=1,2Layer2:I-2)(t)-ai)2/b2andO.epx()i=1,2j=1,2,3.7Layer3:I13)=tu(X2Q)IandFig.3.schematicofneuralnetworksmodelingofsuspensionsystem(4)Sy()=,w.*i(t)+WJD_Xj(t-_1)i1=(i(t)+wjXj(t_lqyj(t)=1wxi(t)j=l(5)(6)1796wherewI,wareweightoftherecurrentneuralnetwork,Xj(t)isoutputofneuronwithlocalfeedbackloopneuroninthehiddenlayer,p,qareinputneuronnumberandfeedbackneuronnumberrespectively.Theactivationfunctionforbothinputneuronsandoutputneuronsislinearfunction,whiletheactivationforneuronsinthehiddenlayerissigmoidfunction.heobjectivefunctionE(t)canbedefmedinthetermsoftheerrorsignale(t)as:E(t)=_y(t)-.y(t)2=1e2(t)(7)22Thatis,E(t)istheinstantaneousvalueoftheerrorenergy.Thestep-by-stepadjustmentstothesynapticweightsofneuronarecontinueduntilthesystemreachsteadystate,i.e.thesynapticweightsareessentiallystabilized.DifferentiatingE(t)withrespecttoweightvectorwyields.aE(t)_8=-e(t)0Y()(8)Fromexpression(1),(2)and(3),differentiatingA(t)0DIwithrespecttotheweightvectorw1w,-,w,-Yrespectivelyyields.aS(t)=x(t)As(t)woax1Q)-(WaXI(t)aWjJaWjFrom(4),(5)and(6),analyzingvalueofsynapticweightisdeterminedbyw(t+1)=w(t)+q*e(t)89(t)(12)whereqtheleaning-rateparameter,Adetailedconvergenceanalysisoftherecurrenttrainingalgorithmisrathercomplicatedtoacquiretheleaning-rateparametervalue.Accordingtoexpression(13),theweightvectorwforrecurrentneuralnetworkcanbeadjusted.WeestablishatheLyapunovfunctionasfollowsV(t)=1/2*e2(t),whosechangevalueAV(t)canbedeterminedaftersometiterations,inthesensethat(13)Wehavenoticedthattheerrorsignale(t)aftersometiterationscanbeexpressedasfollowsfromexpression(13)and(14),ae(t)ao(t)ae(t)ae(t)-,Aw=-qe(t)=77e(t),theawOwawOwLyapunovfunctionincrementcandeterminedaftersometiterationsasfollows(14)Mtt)=-q-&(t)+v2.e(t)-=-V(t)where(t)22jt16(t)2A=10()lpq2-5l0(t)ll2ql2-77O220w(9)?7maxa(t)29ifqf2,thenAV(t)O,wax1(t)DandaWjx1(t)uxiyieldsrespectivelyrecurrentformulas.ax1(t)a-fS(t)FX.x(tt1)1ax1(O)=,WjD=axi(t)aNiafS(t)+wat-i)&4LaNiax1(o)(11)avn=0Havingcomputedthesynapticadjustment,theupdatednamelytherecurrenttrainingalgorithmisconvergent.IV.ROADTESTANDRESULTSANALYSESTomakeademonstrationthevalidityofneuralcontrolalgorithmproposedinthepaper,anexperimentalmini-bussuspensionwithMRfluidshockabsorberhasbeenmanufacturedinChina.Themini-busadaptivesuspensionsystemconsistsofaDSPmicroprocessor,8accelerationsensors,4MRfluidshockabsorbers,and1controllableelectriccurrentpowerwithinputvoltage12V.TheDSPmicroprocessorreceivessuspensionvibrationsignalinputfromaccelerometersmountedrespectivelysprungmassandun-sprungmass.Inaccordancewithvibrationsignalandcontrolschemeinthispaper,theDSPmicroprocessoradjustsdampingofadaptivesuspensionbyapplicationcontrolsignaltothecontrollableelectriccurrentpowerconnectedtoelectromagneticcoilinMRfluidshockabsorbers.MagneticfieldproducedbytheelectromagneticcoilinMRfluidshockabsorberscandvarydampingforceinbothcompressionandreboundbyadjustmentofflow1797II,&V(t)=12(t+1)-e2(t2behaviorsofMRfluidsindampingchannels.Raodtestonmini-busadaptivesuspensionbasedneuralnetworkscontrolpresentedinthispaperarecarriedoutinDclassroadsurfacesrespectivelyinrunningvelocity30,40,50km/h.Duringroadtest,experimentalmini-busrunseachtestconditionataconstantspeed.Thetestexperimentsofadaptivesuspensionwithneuralnetworksandpassivesuspensionsystemwerecarriedoutrepeatedlyundersameroadsurfaceandrunningvelocity.TestresultslistedinTable1haveshownthattheadaptivesuspensionwithneuralnetworkscanreducevibrationpowerspectraldensitiesofbothsprungmassandun-sprungmass.Figure4isthemin-bussuspensionvibrationpowerspectraldensitiesofbothsprungmassandun-sprungmasswithpassiveandadaptivesuspensionsystembyDclassroadsurface.Itisclearthatneuralnetworkscontrolimprovesperformancesofmini-bussuspensionwithmainlyimprovementsoccurringaboutsprungmassresonancepeak.Thepowerspectraldensitiesindicatethattheadaptivesuspensionsystemwithneuralnetworkscontrolcanreducemini-busvibrationgreatlycomparedwithpassivesuspension.Ifexcellentfizzycontrolrulesandrationalmodelingofshockabsorberandsuspensioncanbeobtained,theadaptivesuspensionsystemwithneuralnetworkscontrolwillimprovefartherridecomfortandroadholdingandhandlingstabilityofautomobileinthefuture.TABLEImin-bussuspensionroadtestresults:sprungmassandun-sprungmassaccelerationr.m.s.Values(Dclassroad)Speed30(1km/h)40(1m/h)50(kmlh)PassiveControlreducePassiveControlreducePassiveControlreduce|mass10.37560.325213.40.41400.344916.70.46940.396615.5masspg1.60111426610.91.89751.660312.52.34682.065212.0massIC,-4a|1-#,-t0ri-0110.1.lo1Fry-0Qgco1okaId-ela.r10f1FrcqvOFig.4.min-bussuspensionvibrationpowerspectraldensitiesofsprungmass(left)andun-sprungmass(right)withcontrolandpassive(runningspeed40km/h)V.CONCLUSIONSInthispaper,anewrecurrentneuralnetworks-orientedsuspensionmodelandneurofuzzycontrolschemesforthemini-bussuspensionsystemwereinvestigated.Upontherequirementofusing8accelerationsensors,aDSPcontrollerwithgainschedulingwasdeveloped.ConsideringthecomplexityoftheMRfluidshockabsorber,theactuatordynamicshasbeenincorporatedduringthehardware-in-the-loopsimulations.Itwasdemonstratedthattheadaptivecontrolsystemcould1798achieveacompetitivecontrolperformancebyadoptingtheneurofuzzycontrolschemesandrecurrentneuralnetworks-orientedsuspension.Becausethecontrollawdesign,thegainschedulingstrategy,andthehardware-in-the-loopsimulationmethoddevelopedinthispaperarerestrictedtoamin-bussuspensionsystemwithspecificparameters,theentirestrategycanbeextendedtoothersemi-activesystemifsuspensionparametersarechanged.Roadtestresultsshowthatneurofizzycontrollercaneffectivelyimprovemini-busridecomfortandroadholding.ItisfeasibletoemployDSPcontroltosuppresswholevehiclevibration,includinginsprungmassvibrationandun-sprungmassvibration.Theneurofuzzycontrollershowssomerobustcapabilityandcanminimizeinfluencesonsuspensionmodelparameterschanges,whichareimportantfactorstoimprovecontrolsystemperformance.REFERENCES1KanoppD.(1995)ActiveandSemi-activeVibrationIsolation,TransactionsofASME,JournalofSpecial50thAnniversaryDesignIssue,Vol.117,pp117-125.2Chantrnuwathhana,S.andPeng,H.(1999)AdaptiveRobustControlforActiveSuspension,proceedingsoftheAmericanControlConference,SanDiego,California,pp.l702-17063Yu,F.andCrolla,D.A.(1998)AnOptimalSelf-TuningControllerforActiveSuspension,VehicleSystemDynamics,vol.29,pp.51-654Zadeh,A.,Fahim,A.,andEl-Gindy,M.(1997)NeuralNetworksandFuzzyLogicApplicationstoVehicleSystem,InternationalJournalofVehicleDesign,vol.18(2),pp.132-1935WuweiChen,JamesK.MillsandLeWu,(2003)NeurofuzzyandFuzzyControlofAutomotiveSemi-ActiveSuspensions,InternationalJournalofVehicleAutonomousSystems,vol.1(2),pp.222-2361799
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