智能控制试卷及答案4套

附件1实用标准文案智能控制课程试题A题号—二三四五六七总分分数分数评卷人合分人： 复查人:一、填空题（每空1分，共20分）智能控制系统的基本类型有 和 智能控制具有2个不同于常规控制的本质特点： 和 3．一个理想的智能控制系统应具备的性能等。人工神经网络常见的输出变换函数有人工神经网络的学习规则有： 和 。在人工智能领域里知识表示可以分为.类。二、简答题：（每题5二、简答题：（每题5分，共30分）分数评卷人智能控制系统应具有的特点是什么？智能控制系统的结构一般有哪几部分组成，它们之间存在什么关系？比较智能控制与传统控制的特点。

4．神经元计算与人工智能传统计算有什么不同？5．人工神经元网络的拓扑结构主要有哪几种？分数评卷人6．简述专家系统与传统程序的区别。三、作图题：(每图4分，共20分)画出以下应用场合下适当的隶属函数我们绝对相信巴附近的e(t)是“正小”只有当e(t)足够远离巴时，我们才失去e(t)44是“正小”的信心；我们相信-附近的e(t)是“正大”而对于远离-的e(t)我们很快失去信心；22随着e(t)从王向左移动，我们很快失去信心，而随着e(t)从巴向右移动，我们较慢44失去信心。画出以下两种情况的隶属函数：精确集合A={x仪8<x<^2}的隶属函数；写出单一模糊(singletonfuzzification)隶属函数的数学表达形式，并画出隶属函数图。四、计算题：(每题10四、计算题：(每题10分，共20分)分数评卷人一个模糊系统的输入和输出的隶属函数如图1所示。试计算以下条件和规则的隶属函数：规则1：Iferroriszeroandchang-in-erroriszeroThenforceiszero。均使用最小化操作表示蕴含(usingminimumopertor)；规贝I」2:Iferroriszeroandchang-in-errorispossmallThenforceisnegsmall。均使用乘积操作表示蕴含(usingproductopertor)；2.设论域U u｝，且12345A=0.20.4+0.91+-0.5+-uuuuu12345B=0.10.710.3T++uuuu1345试求AuB,AcB,AC（补集），BC（补集）五、试论述对BP网络算法的改进。（共10分）分数评卷人附件1题号—二三四五六七分数总分分数评卷人 和 合分人： 复查人: 和 一、填空题（每空1分，共20分）1．智能控制的研究对象具备的特点有智能控制系统的主要类型有： TOC\o"1-5"\h\z 、 、 和 。确定隶属函数的方法大致有 、 和 国内外学者提出了许多面向对象的神经网络控制结构和方法，从大类上看，较具代表性的有以下几种： 、 和 。在一个神经网络中，常常根据处理单元的不同处理功能，将处理单元分成有以下三种： 、 和 。专家系统具有三个重要的特征是： 、 分数评卷人和 。二、简答题：（每题5分，共30分）1．智能控制有哪些应用领域？试举例说明其工作原理。2．试说明智能控制的三元结构，并画出展示它们之间关系的示意图。3．模糊逻辑与随机事件的联系与区别。精彩文档4．给出典型的神经元模型。5．BP基本算法的优缺点。分数评卷人6．专家系统的基本组成。三、作图题：(每图4分，共20分)画出以下应用场合下适当的隶属函数随着e(t)从乞向左移动，我们很快失去信心，而随着e(t)从乞向右移动，我们较慢33失去信心。我们相信-附近的e(t)是“正大”而对于远离-的e(t)我们很快失去信心；22我们绝对相信互附近的e(t)是“正小”只有当e(t)足够远离互时，我们才失去e(t)33是“正小”的信心；画出以下两种情况的隶属函数：精确集合A={x炉5<x<%}的隶属函数；写出单一模糊(singletonfuzzification)隶属函数的数学表达形式，并画出隶属函数图。四、计算题：(每题10四、计算题：(每题10分，共20分)分数评卷人一个模糊系统的输入和输出的隶属函数如图1所示。试计算以下条件和规则的隶属函数：(a)规贝I」1:Iferroriszeroandchang-in-errorisnegsmallThenforceispossmall。均使用最小化操作表示蕴含(usingminimumopertor)；(b)规贝I」2:Iferroriszeroandchang-in-errorispossmallThenforceisnegsmall。均使用乘积操作表示蕴含（usingproductopertor）；设论域U u｝设论域U u｝，且123410.5A=+—+-+-uuuuu123450.10.710.3B=T+—-卜——uuuu1345试求AuB,AcB,AC（补集），BC（补集）五、试论述建立专家系统的步骤。（共10五、试论述建立专家系统的步骤。（共10分）分数评卷人题号—二三四五六七总分分数一附件1智能控制课程试题C分数评卷人合分人： 复查人:一、填空题（每空1分，共20分）学科，它具有非常广泛的应用领域，例如 1.智能控制是一门新兴的 学科，它具有非常广泛的应用领域，例如 和.2.传统控制包括 和3．一个理想的智能控制系统应具备的性能等。学习系统的四个基本组成部分是专家系统的基本组成部分是二、简答题：（每题5分，共二、简答题：（每题5分，共30分）智能控制系统的结构一般有哪几部分组成分数评卷人它们之间存在什么关系？智能控制系统有哪些类型，各自的特点是什么？比较智能控制与传统控制的特点。4.根据外部环境所提供的知识信息与学习模块之间的相互作用方式，机器学习可以划分为

哪几种方式？5．建造专家控制系统大体需要哪五个步骤？分数评卷人6．为了把专家系统技术应用于直接专家控制系统，在专家系统设计上必须遵循的原则是什么？三、作图题：(每图4分，共20分)画出以下应用场合下适当的隶属函数我们绝对相信-附近的e(t)是“正小”只有当e(t)足够远离巴时，我们才失去e(t)44是“正小”的信心；我们相信-附近的e(t)是“正大”而对于远离-的e(t)我们很快失去信心；22随着e(t)从王向左移动，我们很快失去信心，而随着e(t)从巴向右移动，我们较慢44失去信心。画出以下两种情况的隶属函数：精确集合A={x仪8<x<%}的隶属函数；写出单一模糊(singletonfuzzification)隶属函数的数学表达形式，并画出隶属函数图。四、计算题：(每题10四、计算题：(每题10分，共20分)分数评卷人一个模糊系统的输入和输出的隶属函数如图1所示。试计算以下条件和规则的隶属函数：(a)规贝I」1:Iferroriszeroandchang-in-erroriszeroThenforceiszero。均使用最小化操作表示蕴含(usingminimumopertor)；(b)规贝I」2:Iferroriszeroandchang-in-errorispossmallThenforceisnegsmall。均使用乘积操作表示蕴含（usingproductopertor）；设论域U u｝，且12345A=02+04+0.9+丄+0.5uu34uu3410.312门0.1 0.7B= + +—+uu45uu4513分数评卷人试求AuB,AcB,AC（补集），BC（补集）五、画出静态多层前向人工神经网络（BP网络）的结构图，并简述BP神经网络的工作过程（10分）

题号—二三四五六七总分分数附件1智能控制课程试题D复查人：合分人：复查人：一、填空题（每空1分，共20一、填空题（每空1分，共20分）分数评卷人1.智能控制是一门新兴的学科，它具有非常广泛的应用领域，例如.和.2．智能控制系统的主要类型有：和 。—个理想的智能控制系统应具备的性智能能是在设计知识表达方法时，必须从表达方法的 、 、 这四个方面全面加以均衡考虑。5．在一个神经网络中，常常根据处理单元的不同处理功能，将处理单元分成输入单元、输出单元和 三类。二、简答题：（每题5二、简答题：（每题5分，共30分）分数评卷人10． 智能控制系统的结构一般有哪几部分组成，它们之间存在什么关系？11. 试说明智能控制的三元结构，并画出展示它们之间关系的示意图。12． 比较智能控制与传统控制的特点。神经网络应具的四个基本属性是什么？神经网络的学习方法有哪些？分数评卷人按照专家系统所求解问题的性质，可分为哪几种类型？三、作图题：(每图4分，共20分)画出以下应用场合下适当的隶属函数我们绝对相信巴附近的e(t)是“正小”只有当e(t)足够远离巴时，我们才失去e(t)22是“正小”的信心；我们相信-附近的e(t)是“正大”而对于远离-的e(t)我们很快失去信心；33随着e(t)从王向左移动，我们很快失去信心，而随着e(t)从巴向右移动，我们较慢44失去信心。画出以下两种情况的隶属函数：精确集合A={x卜4<x 的隶属函数；写出单一模糊(singletonfuzzification)隶属函数的数学表达形式，并画出隶属函数图。四、计算题：(每题10四、计算题：(每题10分，共20分)分数评卷人一个模糊系统的输入和输出的隶属函数如图1所示。试计算以下条件和规则的隶属函数：

(a)规贝I」1:Iferroriszeroandchang-in-erroriszeroThenforceiszero。均

使用最小化操作表示蕴含(usingminimumopertor)；(b)规贝I」2:Iferroriszeroandchang-in-errorispossmallThenforceisnegsmall。均使用乘积操作表示蕴含(usingproductopertor)；设论域U u｝，且12345TOC\o"1-5"\h\z0.4 0.3 0.9 1 0.5A= + + + +-u u u u u12345厂 0.1 0.7 1 0.3B= + + + -uuuu1 3 4 5试求AuB,AcB,AC(补集)，BC(补集)五、试述专家控制系统的工作原理（共10五、试述专家控制系统的工作原理（共10分）分数评卷人Fuzzycontrolofaball-balancingsystemI.IntroductionTheball-balancingsystemconsistsofacartwithanarcmadeoftwoparallelpipesonwhichasteelballrolls.Thecartmovesonapairoftrackshorizontallymountedonaheavysupport(Fig.1).Thecontrolobjectiveistobalancetheballonthetopofthearcandatthesametimeplacethecartinadesiredposition.Itiseducational,becausethelaboratoryrigissufficientlyslowforvisualinspectionofdifferentcontrolstrategiesandthemathematicalmodelissufficientlycomplextobechallenging.Itisaclassicalpendulumproblem,liketheonesusedasabenchmarkproblemforfuzzyandneuralnetcontrollers,assalesmaterialforfuzzydesigntools.Initially,thecartisinthemiddleofthetrackandtheballisontheleftsideofthecurvedarc.Acontrollerpullsthecartlefttogettheballupnearthemiddle,thenthecontrolleradjuststhecartpositionverycarefully,withoutloosingtheball.Fuzzycontrolprovidesaformatmethodologyforrepresenting,manipulatingandimplementingahuman'sheuristicknowledgeabouthowtocontrolasystem[1-3].Here,thefuzzycontroldesignmethodwillbeusedtocontroltheball-balancingsystem.Fig.1Ball-balancinglaboratoryrigDesignobjective.Learningtheoperatingprincipleoftheball-balancingsystem;.Masteringthefuzzycontrolprincipleanddesignprocedure;.Enhancingtheprogrammingpowerusingmatlab.Designrequirements.Balancingtheballonthetopofthearcandatthesametimeplacethecartinadesiredposition..Comparingthecontrolresultofthelinearcontrollerwiththatofthefuzzycontrollerandthinkingabouttheadvantageoffuzzycontroltoconventionalcontrol.DesignprincipleModeldescriptionoftheball-balancingsystemIntroducethestatevectorxofstatevariables(yrepresentscartpositionandPW|S0.22rad)representsballangulardeviation)x=y1x=y2*x=P3x=P4Thenonlinearstate-spaceequations[5]aregivenasfollows:X=X'12

-m(R+r)(-(r+R)mr(sinxcos2x)x2+mgrsinxcosx)X=

*2TOC\o"1-5"\h\za a” a aX=

*23 34 3 3I(R+r) rM(cos2x)m(R+r)(M+m)( +rm(sin2x)(R+r)+ 3—r 3 (M+m)

m(R+r)(x2sinx1(R+r)+x2rm(sin3x)(R+r))+ 4 3r 4 3I(R+r) rM(cos2x)m(R+r)(M+m)( +rm(sm2x)(R+r)+ 3—r 3 (M+m)(r+R)(mr2+1)+ FI(R+r) rM(cos2x)m(R+r)r(M+m)( +rm(sin2x)(R+r)+ 3 )r 3 (M+m)xx•3(-rm2x2R+r(cosxsinx)+mgrsinx)x•4TOC\o"1-5"\h\z4M+m3 3 3x•4I(R+r) rM(cos2x)m(R+r)(M+m)+rm(sm2(M+m)rcos(x) m—rcos(x) m—3M+mFI(R+r) rM(cos2x)m(R+r)+rm(sm2x)(R+r)+ 3-r 3 (M+m)WhereR=0.5mrepresentscartradiusofthearc,M=3.1kgisthecartweight,Frepresentscartdrivingforce,r=0.0275mistheballradius,r=0.025mistheballrollingradius,m=0.675kgistheballweight,I=0.024x10-3istheballmomentofinertiaandg=9.81ms-2representsgravity.Themodelcanbelinearisedaroundtheorigin.Theapproximationstothetrigonometricfunctionsareintroducedasfollowscos申口1,sin申口申,cos2申口1,sin2申口0andthelinearstate-spacemodelcanbeobtainedasfollowsx=Ax+Buy=CxMatricesA,B,Caresimplyandgivenasfollows

__0100--0_00a0bB=0001000c0_d_1000C二00107 mr2+1b=MI+ml7 mr2+1b=MI+ml+mr2Mwitha=——MI+ml+mr2Mmr2gmr2g(M+m)(R+r)(MI+ml+mr2M)d=——(R+r)(MI+ml+mr2M)Theactualvaluesoftheconstantsare(a,b,c,d)=(—1.34,0.301,14.3,—0.386).FuzzycontrollerdesignTherearespecificcomponentscharactersticofafuzzycontrollertosupportadesignprocedure.IntheblockdiagraminFig.2,thefuzzycontrollerhasfourmaincomponents.Thefollowingexplainstheblockdiagram.FuzzycontrollerFig.2FuzzycontrollerarchitectureFuzzycontrollerFig.2FuzzycontrollerarchitectureFuzzificationThefirstcomponentisfuzzification,whichconvertseachpieceofinputdatatodegreesofmembershipbyalookupinoneofseveralmembershipfunctions.Thefuzzificationblockthusmatchestheinputdatawiththeconditionsoftherulestodeterminehowwelltheconditionofeachrulematchesthatparticularinputinstance.RulebaseTherulebasecontainsafuzzylogicquantificationoftheexpert'slinguisticdescriptionofhowtoachievegoodcontrol.InferenceengineForeachrule,theinferenceenginelooksupthemembershipvaluesintheconditionoftherule.AggregationTheaggregationoperationisusedwhencalculatingthedegreeoffulfillmentorfiringstrengthoftheconditionofarule.Aggregationisequivalenttofuzzification,whenthereisonlyoneinputtothecontroller.Aggreagtionissometimesalsocalledfufilmentoftheruleorfiringstrength.ActivationTheactivationofaruleisthedeductionoftheconclusion,possiblyreducedbyitsfiringstrength.Arulecanbeweightedbyaprioribyaweightingfactor,whichisitsdegreeofconfidence.Thedegreeofconfidenceisdeterminedbythedesigner,oralearningprogramtryingtoadapttherulestosomeinput-outputrelationship.AccumulationAllactivatedconclusionsareaccumulatedusingthemaxoperation.DefuzzificationTheresultingfuzzysetmustbeconvertedtoanumberthatcanbesenttotheprocessesasacontrolsignal.Thisoperationiscalleddefuzzification.Theoutputsetscanbesingletons,buttheycanalsobelinearcombinationsoftheinputs,orevenafunctionoftheinputs.TheT-SfuzzymodelwasproposedbyTakagiandSugenoinanefforttodevelopasystematicapproachtogeneratingfuzzyrulesfromagiveninput-outputdataset[4].Itsrulestructurehasthefollowingform:Ri:ifxisAi,xisAi, ,xisAi,thenyi=Pi+Pi+Pix+Pix+ +Pix1122mm011122mmWhereAiisafuzzysetxisthej—thinput,misthenumberofinputsyijjistheoutputspecifiedbytheruleRi，Piisthetruthvalueparameter.Usingfuzzyjinferencebaseduponproduct-sum-gravityatagiveninput,x=[x,x,,x]T，1 2mthefinaloutputofthefuzzymodel,yn(i=1,2,,n)isinferredbyTakingtheweightedaverageofyl艺①iyi乙①ii=1wherenisthenumberoffuzzyrules,theweight,®iimpliestheoveralltruthvalueofthei—thrulecalculatedbasedonthedegreesofmembershipvalues:①i=邛卩(x)Aiji=1 jComputersimulationThesimulationresultscanbeobtainedbythedesignedprogramusingmatlab.Initialconditionscanbechangedandcontrollergainscanbeadjusted.Thenthedesiredresultscanbeobtained.Designprocedure.Themodeloftheball-balancingsystemhasbeengiven;.Fuzzycontrollerdesign;Fuzzycontroldesignessentiallyamountsto(1)choosingthefuzzycontrollerinputsandoutputs(2)choosingthepreprocessingthatisneededforthecontrollerinputsandpossiblypostprocessingthatisneededfortheoutputs,and(3)designingeachofthefourcomponentsofthefuzzycontrollershowninFig.2..Computersimulation.References.K.M.PassinoandS.Yurkovich(1997).Fuzzycontrol,1stedn,AddisionWesleyLongman,Colifornia..CaiZixing.IntelligentControl:Principles,TechniquesandApplications.Singapore-NewJersey:WorldScientificPublishers,Dec.1997..Pedrycz,W.(1993).Fuzzycontrolandfuzzysystems,secondedn,WileyandSons,NewYork..Takagi,T.andSugno,M.(1985).Fuzzyidentificationofsystemsanditsapplicationstomodelingandcontrol,IEEETrans.Systems,Man&Cybernetics15(1):116-132.SpeedcontroldesignforavehiclesystemusingfuzzylogicI.IntroductionEngineandotherautomobilesystemsareincreasinglycontrolledelectronically.Thishasledtoimprovedfueleconomy,reducedpollution,improveddrivingsafetyandreducedmanufacturingcosts.Howevertheautomobileisahostileenvironment:especiallyintheenginecompartment,wherehightemperature,humidity,vibration,electricalinterferenceandafinecocktailofpotentiallycorrosivepollutantsarepresent.Thesehostilefactorsmaycauseelectricalcontactstodeteriorate,surfaceresistancestofallandsensitiveelectronicsystemstofailinavarietyofmodes.Someofthesefailuremodeswillbebenign,whereasothersmaybedangerousandcauseaccidentsandendangertohumanlife.Acruisecontrolsystem,orvehiclespeedcontrolsystemcankeepavehicle'sspeedconstantonlongrunsandthereforemayhelppreventdriverfatigue[2-5].Ifthedriverhandsoverspeedcontroltoacruisecontrolsystem,thenthecapabilityofthesystemtocontrolspeedtothesetvalueisjustascriticaltosafetyasisthecapabilityofthedrivertocontrolspeedmanually.Sothecruisecontrolsystemdesignisimperativeandimportanttoanautomobile.Designrequirements.Designingcontrollerusingfuzzylogic;.Makingtheautomobile'sspeedkeepconstant.ModeldescriptionoftheautomobileThedynamicsoftheautomobile[1]aregivenasfollowsV(t)=丄(-Au2(t)-d+f(t))mpf(t)=1(-f(t)+u(t))TWhereuisthecontrolinput(u>0representsathrottleinputandu<0representsabrakeinput),m=1300kgisthemassofthevehicle,A=0.3Ns2/m2isitsaerodynamicdrag,d=100Nisaconstantfrictionalforce,pfisthedriving/brakingforce,andT=0.2secissaturatedat±1000N).Wecanusefuzzycontrolmethodtodesignacruisecontrolsystem.Obviously,thefuzzycruisecontroldesignobjectiveistodevelopafuzzycontrollerthatregulatesavehicle'sspeedu(t)toadriver-specifiedvalueu(t).dSpeedcontroldesignusingfuzzylogicFuzzycontrollogicandneuralnetworksareotherexamplesofmethodologiescontrolengineersareexaminingtoaddressthecontrolofverycomplexsystems.Agoodfuzzycontrollogicapplicationisincruisecontrolarea.1)DesignofPIfuzzycontrollerSupposethatwewishtobeabletotrackasteporrampchangeinthedriver-specifiedspeedvalueu(t)veryaccurately.A“PIfuzzycontroller”candbeusedasshowninFig.1.InFig.1,thefuzzycontrollerisdenotedby①;ggandgarescalinggains;andb(t)istheinputoftheintegrator.0,12Fig.1SpeedcontrolsystemusingaPIfuzzycontrollerFindthedifferentialequationthatdescribestheclosed-loopsystem.Letthestatebex=[x,x,x]T=[u,f,b]Tandfindasystemofthreefirst-orderordinary123differentialequationsthatcanbeusedbytheRunge-Kuttamethodinthesimulationoftheclosed-loopsystem.①isusedtorepresentthecontrollerinthedifferentialequations.Forthereferenceinput,threedifferenttestsignalscanbeusedasfollows:a:Testinput1makesU(t)=18m/sec(40.3mph)for0<t<10andU(t)=22ddm/sec(49.2mph)for10冬t<30.b:Testinput2makesU(t)=18m/sec(40.3mph)for0<t<10andU(t)ddincreaseslinearly(aramp)from18to22m/secbyt=25sec,andthenU(t)=22dfor255t530.c:Testinput3makesU(t)=22for051andweusex(0)astheinitialdcondition(thisrepresentsstartingthevehicleatrestandsuddenlycommandingalargeincreasespeed).Usex(0)=[18,197.2,20]tfortestinput1and2.Designthefuzzycontroller①togetlessthan2%overshoot,arise-timebetween5and7sec,andasettlingtimeoflessthan8sec(i.e.,reachtowithin2%ofthefinalvaluewithin8sec)forthejumpfrom18to22m/secin“testinput1”thatisdefinedabove.Also,fortherampinput“(testinput2”above)itmusthavelessthan1mph(0.447m/sec)steady-stateerror(i.e.,attheendoftheramppartoftheinputhavelessthan1mpherror).Fullyspecifythecontroller(e.g.,themembershipfunctions,rule-basedefuzzification,etc.)andsimulatetheclosed-loopsystemtodemonstratethatitperformsproperly.ProvideplotsofU(t)andU(t)onthesameaxisandu(t)onadifferentplot.Fortestinput3findthedrise-time,overshoot,2%settlingtime,andsteady-stateerrorfortheclosed-loopsystemforthecontrollerthatyoudesignedtomeetthespecificationsfortestinput1and2.UsingtheRunge-Kuttamethodandintegrationstepsizeof0.01,thesimulationresultscanbeshownasfollows..Testinput1

Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput1Fig.2②Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput1Fig.2②Fig.3Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput2③.Testinput3

Fig.4Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput32)DesignofPDfuzzycontrollerSupposethatyouareconcernedwithtrackingastepchangeinu(t)accuratelydandthatyouusethePDfuzzycontrollershowninFig.5.Torepresentthederivative,simplyuseabackwarddifferencee(t)-e(t-h)c(t)二hWherehistheintegrationstepsizeinyoursimulation(oritcouldbeyoursamplingperiodinanimplementation).Fig.5SpeedcontrolsystemusingaPDfuzzycontrollerDesignaPDfuzzycontrollertogetlessthan2%overshoot,arise-timebetween7and10sec.andasettlingtimeoflessthan10secfortestinput1definedina).Also,fortherampinput(testinput2in1))itmusthavelessthan1mphsteady-stateerrortotheramp(i.e.,attheendoftheramppartoftheinput,havelessthan1mpherror).Fullyspecifyyourcontrollerandsimulatetheclosed-loopsystemtodemonstratethatitperformsproperly.Provideplotsofu(t)andu(t)onthesameaxisanddu(t)onadifferentplot.Inthesimulations,theRunge-Kuttamethodisusedandanintegrationstepsizeof0.01.Assumethatx(0)=[1&197.2]Tfortestinputs1and2(henceweignorethederivativeinputincomingupwiththestateequationsfortheclosed-loopsystemandsimplyusetheapproximationforc(t)thatisshownabovesothatwehaveatwo-statesystem).Asafinaltestletx(0)=0andusetestinput3definedin1).①.Testinput1Fig.6input1Fig.6input1.Testinput2Fig.7Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput2③.Testinput3Fig.8Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput3V.SummaryTokeepanautomobile'sspeedconstant,aspeedcontroldesignmethodusingfuzzylogicispresented.PIfuzzycontrollerandPDfuzzycontrollerdesignschemesaregiventoregulateavehicle'sspeedtoadriver-specifiedvalue.Thesimulationresultsshowthevalidityand