車輛工程外文翻譯-汽車主動懸架系統(tǒng)的神經(jīng)網(wǎng)絡(luò)控制運(yùn)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(tǒng)的神經(jīng)網(wǎng)絡(luò)控制運(yùn)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(tǒng),神經(jīng)網(wǎng)絡(luò),控制,運(yùn)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(tǒng)的神經(jīng)網(wǎng)絡(luò)控
制運(yùn)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(tǒng)的神經(jīng)網(wǎng)絡(luò)控制運(yùn)算法則的研究
L.J.Fu, J.G.Cao 重慶工學(xué)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(shù)學(xué)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(yīng)不同路面狀況和汽車運(yùn)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(jié)構(gòu)復(fù)雜并且消極懸架無法滿足各種路面條件和汽車運(yùn)行狀態(tài)的要求,因此半可控懸架系統(tǒng)是目前最常用的懸架系統(tǒng)。本文將著重介紹自適應(yīng)神經(jīng)控制的汽車懸架循環(huán)神經(jīng)網(wǎng)絡(luò)模擬控制器。懸架系統(tǒng)神經(jīng)網(wǎng)絡(luò)不同于汽車懸架的動態(tài)參數(shù),并且還能夠?yàn)樯窠?jīng)自動調(diào)節(jié)控制器提供學(xué)習(xí)信號,為了檢驗(yàn)控制結(jié)果，在DSP微處理系統(tǒng)基礎(chǔ)上為中巴安裝液壓減振器和多維控制系統(tǒng),并在各種速度和路面上進(jìn)行實(shí)驗(yàn).將此控制結(jié)果和開環(huán)消極懸架系統(tǒng)進(jìn)行比較,結(jié)果表明神經(jīng)網(wǎng)絡(luò)控制運(yùn)算在減少微型客車振動方面表現(xiàn)的非常良好。
1．概述
汽車懸架系統(tǒng)的主要功用是支撐車身的重量,并且使汽車穩(wěn)定有效的進(jìn)行轉(zhuǎn)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(dǎo)致設(shè)計(jì)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(yùn)行條件的阻尼離的自動減振器組成。由于主動懸架結(jié)構(gòu)復(fù)雜而傳統(tǒng)的消極式懸架無法滿足不同路面狀況和汽車運(yùn)行狀況的要求。因此,半自動懸架是目前最常用的懸架系統(tǒng)。半自動懸架系統(tǒng)的優(yōu)點(diǎn)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(tǒng)是為半自動懸架系統(tǒng)而開發(fā)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(zhì)量成一定比例的。許多調(diào)查都是用一維模型,它可以推導(dǎo)出模糊的控制點(diǎn)和控制運(yùn)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(dǎo)致這種控制方法不能充分發(fā)揮半自動懸架的功用。為充分利用懸架系統(tǒng)的非線性功用。如模糊邏輯控制。神經(jīng)網(wǎng)絡(luò)控制和模糊神經(jīng)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(tǒng)控制[4,5]。
本文，一種神經(jīng)自適應(yīng)控制控制器被用于控制汽車懸架神經(jīng)網(wǎng)絡(luò)和瞬邊的MR減振器的循環(huán)振動?？刂破鞯慕Y(jié)構(gòu)設(shè)計(jì)和控制運(yùn)算法則將在第2部分進(jìn)行詳細(xì)敘述。懸架的循環(huán)神經(jīng)網(wǎng)絡(luò)動態(tài)模擬在第3部分進(jìn)行介紹控制系統(tǒng)實(shí)驗(yàn)在第4部分，第5部分是總結(jié)。
1. 汽車懸架的多維自調(diào)節(jié)控制法則
神經(jīng)模糊控制系統(tǒng)將在本文進(jìn)行介紹，由圖1可知，它是由模糊神經(jīng)網(wǎng)絡(luò)和神經(jīng)網(wǎng)絡(luò)模型構(gòu)成的微型客車懸架。神經(jīng)網(wǎng)絡(luò)模糊控制即自適應(yīng)控制，它有學(xué)習(xí)和控制的功能。它的循環(huán)神經(jīng)網(wǎng)絡(luò)功用是用來鑒別中巴車懸架的模擬參數(shù)。圖1中的y(t)和yd(t)分別是系統(tǒng)實(shí)際輸出和系統(tǒng)理想輸出。xl(t)是系統(tǒng)實(shí)際輸出和理想輸出之間的誤差。x2(t)是系統(tǒng)實(shí)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(jīng)網(wǎng)絡(luò)控制系統(tǒng)的結(jié)構(gòu)
網(wǎng)絡(luò)控制系統(tǒng)：整體集的定義分別如下： = [- E,E], = [- E,E], =[-U,U].神經(jīng)模糊控制器有四層神經(jīng)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(shù)解決模糊集,和模糊集的組成，其函數(shù)的第一如下：
圖2.自動懸架神經(jīng)網(wǎng)絡(luò)控制器簡圖
，由圖2可知，輸入/輸出如下：1：
和
和
都是神經(jīng)網(wǎng)絡(luò)的輸入部分。是其重量，是其輸出部分，，都是高斯函數(shù)的重要值。神經(jīng)網(wǎng)絡(luò)控制器的學(xué)習(xí)法則是以斜率誤差信號逆向傳遞方法為基礎(chǔ)的。誤差逆向傳遞方法通過使函數(shù)[5]損失降至最低自動調(diào)節(jié)重量。
3．懸架循環(huán)神經(jīng)網(wǎng)絡(luò)動態(tài)模擬法則
懸架神經(jīng)網(wǎng)絡(luò)設(shè)計(jì)用于將實(shí)際輸出量通過第三層神經(jīng)網(wǎng)近似反饋給潛在的循環(huán)層，結(jié)構(gòu)如圖3所示。其性能是使循環(huán)神經(jīng)網(wǎng)絡(luò)能夠自動獲知周圍環(huán)境并且據(jù)此提高其重量自動適應(yīng)作用.循環(huán)神經(jīng)網(wǎng)絡(luò)輸入信號和和潛在層的邏輯反饋循環(huán)神經(jīng)的輸出量的總輸出量對等于神經(jīng)。
圖3.懸架系統(tǒng)神經(jīng)網(wǎng)絡(luò)模擬簡圖。
是循環(huán)神經(jīng)網(wǎng)絡(luò)的負(fù)荷，是潛在層邏輯循環(huán)反饋神經(jīng)的輸出神經(jīng)量，分別是輸入神經(jīng)量和反饋神經(jīng)量。激活函數(shù)是輸入函數(shù)和輸出函數(shù)的線性函數(shù)，潛在層神經(jīng)的激活是S形的函數(shù)。
它的反函數(shù)通過誤差信號定義如下：
是誤差能量的瞬時值.神經(jīng)元的突出質(zhì)量一步一步連續(xù)的自動調(diào)節(jié)直至系統(tǒng)達(dá)到 穩(wěn)定狀態(tài)，即突出質(zhì)量基本上穩(wěn)定。
從式1，2和3可知：
從4，5和6分析和分別推導(dǎo)出循環(huán)分子式。
突出質(zhì)量可以由下式計(jì)算得到：
· 是速率參數(shù)，詳細(xì)分析循環(huán)算法獲得速率參數(shù)值是相當(dāng)復(fù)雜的。根據(jù)式13得，循環(huán)神經(jīng)網(wǎng)絡(luò)質(zhì)量矢量能夠自動調(diào)節(jié)。函數(shù)如下，其變值經(jīng)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(shù)增量經(jīng)過t時間可以定義為：
.
4 .路面測試結(jié)果分析
神經(jīng)控制運(yùn)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(jīng)大量投產(chǎn)制造. 微型客車自適應(yīng)懸架系統(tǒng)由一個DSP微處理系統(tǒng)，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(huán)電流控制器組成.DSP微處理器通過傳感器獲取懸架彈簧負(fù)載和空載時候的懸架振動信號.根據(jù)振動信號和本文的控制圖，DSP微處理系統(tǒng)通過調(diào)節(jié)控制信號來調(diào)節(jié)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(chǎn)生的磁場能夠在壓縮沖程和反彈過程中調(diào)節(jié)MR液壓減振器中流體運(yùn)行狀態(tài)。
本文描述的是以神經(jīng)網(wǎng)絡(luò)控制為基礎(chǔ)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(yùn)行。自適應(yīng)懸架分別以神經(jīng)網(wǎng)絡(luò)和消極懸架系統(tǒng)在同樣的路面和運(yùn)行速度下進(jìn)行測試實(shí)驗(yàn)。表1的測試結(jié)果表明神經(jīng)網(wǎng)絡(luò)控制自適應(yīng)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(yīng)微型客車懸架在D級路面上的振動曲線圖。很明顯神經(jīng)網(wǎng)絡(luò)控制主要提高減緩振動的能力。受力曲線圖表明自適應(yīng)懸架系統(tǒng)和消極懸架系統(tǒng)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(jīng)網(wǎng)絡(luò)控制的自適應(yīng)懸架系統(tǒng)遠(yuǎn)乘舒適性能和路面穩(wěn)定保持性能。
表1 微型客車懸架路面測試結(jié)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(jié)論
本文中主要講述的是微型客車的一種新型的循環(huán)神經(jīng)網(wǎng)絡(luò)模型和模糊神經(jīng)控制原理.根據(jù)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(fù)雜性，動態(tài)參數(shù)載入硬盤進(jìn)行仿真.它表明自適應(yīng)控制系統(tǒng)可以通過模糊神經(jīng)控制和循環(huán)神經(jīng)網(wǎng)絡(luò)懸架達(dá)到完全控制作用。由于控制法設(shè)計(jì)，增益調(diào)度策略和硬件循環(huán)仿真的開發(fā)本文限于微型客車的具體參數(shù)，在懸架參數(shù)變化的情況下此方法可以延伸到其它半主動懸架系統(tǒng).路面實(shí)驗(yàn)結(jié)果表明模糊神經(jīng)控制可以有效改善微型客車行使的舒適性和穩(wěn)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(jīng)控制器可以減少對對控制系統(tǒng)性能影響很大的模擬參數(shù)的變化。
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[5] Wuwei Chen, James K. Mills and Le Wu,(2003) Neurofuzzy and Fuzzy Control of Automotive Semi-Active Suspensions, International Journal of Vehicle Autonomous Systems, vol.1 (2), pp.222-236
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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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