.1第七章模型预测控制算法之一~5杨根科上海交通大学自动化系2005年9月.2内容提要概述动态矩阵控制动态矩阵控制的进一步讨论模型算法控制应用.3思想Z域设计(直接设计)时域设计现代控制理论(基于状态空间):如最优控制、极点配置等应用航空航天(理想环境)工业过程控制(基于模型预测):预测控制!石油、化工、发电等先进控制算法.4工业过程特点数学模型的非精确性不确定性、随机性、参数时变性material,energybalancesflowdynamicsphysicalproperties(oftenunknown)thermodynamics多变量系统控制算法的可实施性历史,。。。脉冲响应与阶跃响应;滚动.5模型预测控制,动态复杂性Figure:Twoprocessesexhibitingunusualdynamicbehavior.(a)changeinbaselevelduetoastepchangeinfeedratetoadistillationcolumn(分裂蒸馏塔).(b)steamtemperaturechangeduetoswitchingonsootblower(吹灰器)inaboiler..6模型预测控制,方法ModelPredictiveControl(MPC)–regulatorycontrolsthatuseanexplicitdynamicmodeloftheresponseofprocessvariablestochangesinmanipulatedvariablestocalculatecontrol“moves”.Controlmovesareintendedtoforcetheprocessvariablestofollowapre-specifiedtrajectoryfromthecurrentoperatingpointtothetarget.Basecontrolactiononcurrentmeasurementsandfuture.7模型预测控制,优点OptimalcontrollerisbasedonminimizingerrorfromsetpointBasicversionuseslinearmodel,buttherearemanypossiblemodelsCorrectionsforunmeasureddisturbances,modelerrorsareincludedSinglestepandmulti-stepversions•Treatsmultivariablecontrol,feedforwardcontrol.8模型预测控制,优点/特点ProcessesaredifficulttocontrolwithstandardPIDalgorithm–longtimeconstants,substantialtimedelays,inverseresponse,etc.Thereissubstantialdynamicinteractionamongcontrols,i.e.,morethanonemanipulatedvariablehasasignificanteffectonanimportantprocessvariable.Constraints(limits)onprocessvariablesandmanipulatedvariablesareimportantfornormalcontrol..9模型预测控制Techniquesdevelopedbyindustry:1.DynamicMatrixControl(DMC)-ShellDevelopmentCo.,CutlerandRamaker(1980),-CutlerlaterformedDMC,Inc.-DMCacquiredbyAspentechin1997.2.ModelAlgorithmicControl(MAC)•ADERSA/GERBIOS,Richaletetal(1978)•Over4000applicationsofMPCsince1980(QinandBadgwell,1998and2003)..10模型预测控制,优点/特点Time-delaycompensationtechniquespredictprocessoutputonetimedelayahead.Hereweareconcernedwithpredictivecontroltechniquesthatpredicttheprocessoutputoveralongertimehorizon.(e.g.,open-loopresponsetime)..11模型预测控制,ideaBasicconceptforModelPredictiveControl.12模型预测控制,优点/特点GeneralCharacteristics•Targets(setpoints)selectedbyreal-timeoptimizationsoftwarebasedoncurrentoperatingandeconomicconditions•Minimizesquareofdeviationsbetweenpredictedfutureoutputsandspecificreferencetrajectorytonewtargets•Discretestepresponsemodel•Frameworkhandlesmultipleinput,multipleoutput(MIMO)controlproblems..13模型预测控制,优点/特点Canincludeequalityandinequalityconstraintsoncontrolledandmanipulatedvariables•Solvesaquadraticprogrammingproblemateachsamplinginstant•Disturbanceisestimatedbycomparingtheactualcontrolledvariablewiththemodelprediction•UsuallyimplementsthefirstmoveoutofMcalculatedmoves.141动态矩阵控制(2)动态矩阵控制原理与算法控制结构组成优化策略反馈校正算法设计参数选择采样周期优化时域长度控制时域程度权矩阵误差校正向量.151动态矩阵控制()DMC依靠对象的阶跃响应控制结构组成预测模型a2a1123N-1Na3aN-1aN模型截断离散的阶跃响应.161动态矩阵控制()StepResponseModelsStepresponsemodelsarebasedonthefollowingidea.Assumethatthesystemisatrest.Foralineartime-invariantsingle-inputsingle-output(SISO)systemlettheoutputchangeforaunitinputchangebegivenby{0,a1,a2,...,aN,aN,...}HereweassumethatthesystemsettlesexactlyafterNsteps.Thestepresponse{a1,a2,...,aN}constitutesacompletemodelofthesystem,whichallowsustocomputethesystemoutputforanyinputsequence:可加性)1()()(1NkuaikuakyNNii.171动态矩阵控制()预测模型对象的动态特性通过动态系数{a1,a2,…,aN}来描述{a1,a2,…,aN}是阶跃响应在采样时刻t=T,2T,…,NT的值(aistepresponsecoefficients)(ai+1-aiimpulseresponsecoefficients)N的选择应该使ai~as,(jN),as是阶跃响应终止值.181动态矩阵控制()Forexampleifthediscretesystemdescription(samplingtimeT=0.1)isy(k)=–0.5y(k–1)+u(k–3)thenthetransferfunctionis.191动态矩阵控制()Thefollowingcommandsgeneratethestepresponsemodelforthissystemandplotit:num=[01];den=[10.5];delt1=0.1;delay=2;%whydon’twechoose3?g=poly2tfd(num,den,delt1,delay);%Setupthemodelintfformattfinal=1.6;delt2=delt1;nout=1;plant=tfd2step(tfinal,delt2,nout,g);%Calculatethestepresponseplotstep(plant)%Plotthestepresponse.201动态矩阵控制().211动态矩阵控制()plant=0---a10---a21.0000---a30.50000.75000.62500.68750.65630.67190.66410.66800.66600.66700.66650.66670.6666---a161.0000---nout(i)=1ifoutputiisstable.1.0000---ny0.1000---delt2.221动态矩阵控制()预测模型线性系统的可加性,可以由a=[a1,a2,…,aN]T描述假设t=kT有一个控制增量Δu(k),后面控制量不变在采样时刻t=(k+1)T,(k+2)T,…,(k+N)T的值)|(~)|2(~)|1(~)(~0000kNkykkykkykyN)|(~)|2(~)|1(~)(~1111kNkykkykkykyN)()(~)(~01kuakykyNN.231动态矩阵控制()1预测模型假设t=kT,(k+1)T,…,(k+M-1)T有控制增量Δu(k),Δu(k+1),…,Δu(k-M+1);后面控制量不变在采样时刻t=(k+1)T,(k+2)T,…,(k+P)T的值(滚动))|(~)|2(~)|1(~)(~0000kPkykkykkykyP)|(~)|2(~)|1(~)(~kPkykkykkykyMMMPM)()(~)(~0kuAkykyMPPM1112100000;)1()()(MPPPMaaaaaaAMkukuku.241动态矩阵控制()2优化策略权矩阵:期望轨迹:优化控制变量:ΔuM(k)期望指标(无约束问题):误差小,控制量小,即MPrrrRqqqQ,...,,;,...,,2121)(),...,1()(PkWkWkWP221212)()(~)()(min)1())|(~)(()(minRMQPMPMiiPiMikukykWkJorikurkikyikWqkJ.251动态矩阵控制()优化策略优化控制量:ΔuM(k)TPoPTTMMMPoPTMMMMMPoPTMPoPMiiPiMiMiiPiMiAAxxthatNotekykWQARQAAkukuRkuAkykWQAkukJkukJkuRkukuAkykWQku