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YUJIANG

TheMathWorks,Inc.

ZHONG-PINGJIANG

NewYorkUniversity

Copyright©2017byTheInstituteofElectricalandElectronicsEngineers,Inc.

PublishedbyJohnWiley&Sons,Inc.,Hoboken,NewJersey. PublishedsimultaneouslyinCanada.

LibraryofCongressCataloging-in-PublicationDataisavailable.

ISBN:978-1-119-13264-6

PrintedintheUnitedStatesofAmerica.

GLOSSARYxix

1INTRODUCTION1

1.1FromRLtoRADP / 1

1.2SummaryofEachChapter / 5

References / 6

2ADAPTIVEDYNAMICPROGRAMMINGFORUNCERTAIN LINEARSYSTEMS11

2.1ProblemFormulationandPreliminaries / 11

2.2OnlinePolicyIteration / 14

2.3LearningAlgorithms / 16

2.4Applications / 24

2.5Notes / 29

References / 30

3SEMI-GLOBALADAPTIVEDYNAMICPROGRAMMING35

3.1ProblemFormulationandPreliminaries / 35

3.2Semi-GlobalOnlinePolicyIteration / 38

3.3Application / 43

3.4Notes / 46

References / 46

4GLOBALADAPTIVEDYNAMICPROGRAMMINGFOR NONLINEARPOLYNOMIALSYSTEMS49

4.1ProblemFormulationandPreliminaries / 49

4.2RelaxedHJBEquationandSuboptimalControl / 52

4.3SOS-BasedPolicyIterationforPolynomialSystems / 55

4.4GlobalADPforUncertainPolynomialSystems / 59

4.5ExtensionforNonlinearNon-PolynomialSystems / 64

4.6Applications / 70

4.7Notes / 81

References / 81

5ROBUSTADAPTIVEDYNAMICPROGRAMMING85

5.1RADPforPartiallyLinearCompositeSystems / 86

5.2RADPforNonlinearSystems / 97

5.3Applications / 103

5.4Notes / 109

References / 110

6ROBUSTADAPTIVEDYNAMICPROGRAMMINGFOR LARGE-SCALESYSTEMS113

6.1StabilityandOptimalityforLarge-ScaleSystems / 113

6.2RADPforLarge-ScaleSystems / 122

6.3ExtensionforSystemswithUnmatchedDynamicUncertainties / 124

6.4ApplicationtoaTen-MachinePowerSystem / 128

6.5Notes / 132

References / 133

7ROBUSTADAPTIVEDYNAMICPROGRAMMINGASA THEORYOFSENSORIMOTORCONTROL137

7.1ADPforContinuous-TimeStochasticSystems / 138

7.2RADPforContinuous-TimeStochasticSystems / 143

7.3NumericalResults:ADP-BasedSensorimotorControl / 153

7.4NumericalResults:RADP-BasedSensorimotorControl / 165

7.5Discussion / 167

7.6Notes / 172

References / 173

ABASICCONCEPTSINNONLINEARSYSTEMS177

A.1LyapunovStability / 177

A.2ISSandtheSmall-GainTheorem / 178

BSEMIDEFINITEPROGRAMMINGANDSUM-OF-SQUARES PROGRAMMING181

B.1SDPandSOSP / 181 CPROOFS183

C.1ProofofTheorem3.1.4 / 183

C.2ProofofTheorem3.2.3 / 186

References / 188

INDEX191

PREFACEANDACKNOWLEDGMENTS

Thisbookcoversthetopicofadaptiveoptimalcontrol(AOC)forcontinuous-time systems.Anadaptiveoptimalcontrollercangraduallymodifyitselftoadapttothe controlledsystem,andtheadaptationismeasuredbysomeperformanceindexof theclosed-loopsystem.ThestudyofAOCcanbetracedbacktothe1970s,when researchersattheLosAlamosScientificLaboratory(LASL)startedtoinvestigate theuseofadaptiveandoptimalcontroltechniquesinbuildingswithsolar-based temperaturecontrol.Comparedwithconventionaladaptivecontrol,AOChasthe importantabilitytoimproveenergyconservationandsystemperformance.However, eventhoughtherearevariouswaysinAOCtocomputetheoptimalcontroller,most ofthepreviouslyknownapproachesaremodel-based,inthesensethatamodel withafixedstructureisassumedbeforedesigningthecontroller.Inaddition,these approachesdonotgeneralizetononlinearmodels.

Ontheotherhand,quiteafewmodel-free,data-drivenapproachesforAOChave emergedinrecentyears.Inparticular,adaptive/approximatedynamicprogramming (ADP)isapowerfulmethodologythatintegratestheideaofreinforcementlearning (RL)observedfrommammalianbrainwithdecisiontheorysothatcontrollersfor man-madesystemscanlearntoachieveoptimalperformanceinspiteofuncertainty abouttheenvironmentandthelackofdetailedsystemmodels.Sincethe1960s,RL hasbeenbroughttothecomputerscienceandcontrolscienceliteratureasaway tostudyartificialintelligence,andhasbeensuccessfullyappliedtomanydiscretetimesystems,orMarkovDecisionProcesses(MDPs).However,ithasalwaysbeen challengingtogeneralizethoseresultstothecontrollerdesignofphysicalsystems. Thisismainlybecausethestatespaceofaphysicalcontrolsystemisgenerally continuousandunbounded,andthestatesarecontinuousintime.Therefore,the convergenceandthestabilitypropertieshavetobecarefullystudiedforADP-based

approaches.Themainpurposeofthisbookistointroducetherecentlydeveloped framework,knownasrobustadaptivedynamicprogramming(RADP),fordatadriven,non-modelbasedadaptiveoptimalcontroldesignforbothlinearandnonlinear continuous-timesystems.

Inaddition,thisbookisintendedtoaddressinasystematicwaythepresenceof dynamicuncertainty.Dynamicuncertaintyexistsubiquitouslyincontrolengineering. Itisprimarilycausedbythedynamicswhicharepartofthephysicalsystembutare eitherdifficulttobemathematicallymodeledorignoredforthesakeofcontroller designandsystemanalysis.Withoutaddressingthedynamicuncertainty,controller designsbasedonthesimplifiedmodelwillmostlikelyfailwhenappliedtothe physicalsystem.InmostofthepreviouslydevelopedADPorotherRLmethods, itisassumedthatthefull-stateinformationisalwaysavailable,andthereforethe systemordermustbeknown.Althoughthisassumptionexcludestheexistenceof anydynamicuncertainty,itisapparentlytoostrongtoberealistic.Foraphysical modelonarelativelylargescale,knowingtheexactnumberofstatevariablescan bedifficult,nottomentionthatnotallstatevariablescanbemeasuredprecisely. Forexample,considerapowergridwithamaingeneratorcontrolledbytheutility companyandsmalldistributedgenerators(DGs)installedbycustomers.Theutility companyshouldnotneglectthedynamicsoftheDGs,butshouldtreatthemas dynamicuncertaintieswhencontrollingthegrid,suchthatstability,performance, andpowersecuritycanbealwaysmaintainedasexpected.

Thebookisorganizedinfourparts.First,anoverviewofRL,ADP,andRADP iscontainedinChapter1.Second,afewrecentlydevelopedcontinuous-timeADP methodsareintroducedinChapters2,3,and4.Chapter2coversthetopicofADPfor uncertainlinearsystems.Chapters3and4provideneuralnetwork-basedandsum-ofsquares(SOS)-basedADPmethodologiestoachievesemi-globalandglobalstabilizationforuncertainnonlinearcontinuous-timesystems,respectively.Third,Chapters5 and6focusonRADPforlinearandnonlinearsystems,withdynamicuncertainties rigorouslyaddressed.InChapter5,differentrobustificationschemesareintroduced toachieveRADP.Chapter6furtherextendstheRADPframeworkforlarge-scalesystemsandillustratesitsapplicabilitytoindustrialpowersystems.Finally,Chapter7 appliesADPandRADPtostudythesensorimotorcontrolofhumans,andtheresults suggestthathumansmaybeusingverysimilarapproachestolearntocoordinate movementstohandleuncertaintiesinourdailylives.

Thisbookmakesamajordeparturefrommostexistingtextscoveringthesame topicsbyprovidingmanypracticalexamplessuchaspowersystemsandhuman sensorimotorcontrolsystemstoillustratetheeffectivenessofourresults.Thebook usesMATLABineachchaptertoconductnumericalsimulations.MATLABisused asacomputationaltool,aprogrammingtool,andagraphicaltool.Simulink,a graphicalprogrammingenvironmentformodeling,simulating,andanalyzingmultidomaindynamicsystems,isusedinChapter2.Thethird-partyMATLAB-based softwareSOSTOOLSandCVXareusedinChapters4and5tosolveSOSprogramsandsemidefiniteprograms(SDP).AllMATLABprogramsandtheSimulink modeldevelopedinthisbookaswellasextensionoftheseprogramsareavailableat http://yu-jiang.github.io/radpbook/

Thedevelopmentofthisbookwouldnothavebeenpossiblewithoutthesupport andhelpofmanypeople.TheauthorswishtothankProf.FrankLewisandDr.Paul Werboswhoseseminalworkonadaptive/approximatedynamicprogramminghaslaid downthefoundationofthebook.Thefirst-namedauthor(YJ)wouldliketothankhis Master’sThesisadviserProf.JieHuangforguidinghimintotheareaofnonlinear control,andDr.YebinWangforofferinghimasummerresearchinternshippositionat MitsubishiElectricResearchLaboratories,wherepartsoftheideasinChapters4and 5wereoriginallyinspired.Thesecond-namedauthor(ZPJ)wouldliketoacknowledge hiscolleagues—speciallyDrs.AlessandroAstolfi,LeiGuo,IvenMareels,andFrank Lewis—formanyusefulcommentsandconstructivecriticismonsomeoftheresearch summarizedinthebook.Heisgratefultohisstudentsfortheboldnessinentering theinterestingyetstillunpopularfieldofdata-drivenadaptiveoptimalcontrol.The authorswishtothanktheeditorsandeditorialstaff,inparticular,MengchuZhou, MaryHatcher,BradyChin,SureshSrinivasan,andDivyaNarayanan,fortheirefforts inpublishingthebook.WethankTaoBianandWeinanGaoforcollaborationon generalizationsandapplicationsofADPbasedontheframeworkofRADPpresented inthisbook.Finally,wethankourfamiliesfortheirsacrificeinadaptingtoourhardto-predictworkingschedulesthatofteninvolvedynamicuncertainties.Fromour familymembers,wehavelearnedtheimportanceofexplorationnoiseinachieving thedesiredtrade-offbetweenrobustnessandoptimality.Thebulkofthisresearchwas accomplishedwhilethefirst-namedauthorwasworkingtowardhisPh.D.degreeinthe ControlandNetworksLabatNewYorkUniversityTandonSchoolofEngineering. TheauthorswishtoacknowledgetheresearchfundingsupportbytheNational ScienceFoundation.

YuJiang Wellesley,Massachusetts Zhong-PingJiang Brooklyn,NewYork

ACRONYMS

ADPAdaptive/approximatedynamicprogramming

AOCAdaptiveoptimalcontrol

AREAlgebraicRiccatiequation

DFDivergentforcefield

DGDistributedgenerator/generation

DPDynamicprogramming

GASGlobalasymptoticstability

HJBHamilton-Jacobi-Bellman(equation)

IOSInput-to-outputstability

ISSInput-to-statestability

LQRLinearquadraticregulator

MDPMarkovdecisionprocess

NFNull-field

PEPersistentexcitation

PIPolicyiteration

RADPRobustadaptivedynamicprogramming

RLReinforcementlearning

SDPSemidefiniteprogramming

SOSSum-of-squares

SUOStrongunboundednessobservability

VFVelocity-dependentforcefield

VIValueiteration

GLOSSARY

| ⋅ |

‖ ⋅ ‖

 1

J ⊕ D

J ⊙ D

TheEuclideannormforvectors,ortheinducedmatrixnormformatrices

Foranypiecewisecontinuousfunction u : ℝ+ → ℝm , ‖u‖ = sup{|u(t )|, t ≥ 0}

Kroneckerproduct

Thesetofallcontinuouslydifferentiablefunctions

Thecostforthecoupledlarge-scalesystem

Thecostforthedecoupledlarge-scalesystem

Thesetofallfunctionsin  1 thatarealsopositivedefiniteandradially unbounded

(⋅)Infinitesimalgenerator

Thesetofallrealnumbers

ℝ+ Thesetofallnon-negativerealnumbers

ℝ[x]d1 ,d2

Thesetofallpolynomialsin x ∈ ℝn withdegreenolessthan d1 > 0and nogreaterthan d2

vec(⋅)vec(A)isdefinedtobethe mn-vectorformedbystackingthecolumns of A ∈ ℝn×m ontopofanother,thatis,vec(A) = [aT 1 aT 2 ⋯ aT m ]T ,where ai ∈ ℝn ,with i = 1,2, … , m,arethecolumnsofA

ℤ+ Thesetofallnon-negativeintegers

[x]d1 ,d2

Thevectorofall ( n + d2 d2 ) ( n + d1 1 d1 1 ) distinctmonicmonomials in x ∈ ℝn withdegreenolessthan d1 > 0andnogreaterthan d2

∇∇V referstothegradientofadifferentiablefunction V : ℝn → ℝ

CHAPTER1

INTRODUCTION

1.1FROMRLTORADP

1.1.1IntroductiontoRL

Reinforcementlearning(RL)isoriginallyobservedfromthelearningbehaviorin humansandothermammals.ThedefinitionofRLvariesindifferentliterature.Indeed, learningacertaintaskthroughtrial-and-errorcanbeconsideredasanexampleof RL.Ingeneral,anRLproblemrequirestheexistenceofan agent,thatcaninteract withsomeunknown environment bytaking actions,andreceivinga reward fromit. SuttonandBartoreferredtoRLas howtomapsituationstoactionssoastomaximize anumericalrewardsignal [47].Apparently,maximizingarewardisequivalentto minimizinga cost,whichisusedmorefrequentlyinthecontextofoptimalcontrol [32].Inthisbook,amappingbetweensituationsandactionsiscalleda policy,and thegoalofRListolearnanoptimalpolicysuchthatapredefinedcostisminimized.

Asauniquelearningapproach,RLdoesnotrequireasupervisortoteachanagent totaketheoptimalaction.Instead,itfocusesonhowtheagent,throughinteractions withtheunknownenvironment,shouldmodifyitsownactionstowardtheoptimal one(Figure1.1).AnRLiterationgenerallycontainstwomajorsteps.First,theagent evaluatesthecostunderthecurrentpolicy,throughinteractingwiththeenvironment. Thisstepisknownas policyevaluation.Second,basedontheevaluatedcost,the agentadoptsanewpolicyaimingatfurtherreducingthecost.Thisisthestepof policyimprovement RobustAdaptiveDynamicProgramming,FirstEdition.YuJiangandZhong-PingJiang. ©2017byTheInstituteofElectricalandElectronicsEngineers,Inc.Published2017byJohnWiley&Sons,Inc.

FIGURE1.1 IllustrationofRL.Theagenttakesanactiontointeractwiththeunknown environment,andevaluatestheresultingcost,basedonwhichtheagentcanfurtherimprove theactiontoreducethecost.

Asanimportantbranchinmachinelearningtheory,RLhasbeenbroughttothe computerscienceandcontrolscienceliteratureasawaytostudyartificialintelligenceinthe1960s[37,38,54].Sincethen,numerouscontributionstoRL,froma controlperspective,havebeenmade(see,e.g.,[2,29,33,34,46,53,56]).Recently, AlphaGo,acomputerprogramdevelopedbyGoogleDeepMind,isabletoimprove itselfthroughreinforcementlearningandhasbeatenprofessionalhumanGoplayers [44].Itisbelievedthatsignificantattentionwillcontinuouslybepaidtothestudy ofreinforcementlearning,sinceitisapromisingtoolforustobetterunderstandthe trueintelligenceinhumanbrains.

1.1.2IntroductiontoDP

Ontheotherhand,dynamicprogramming(DP)[4]offersatheoreticalwaytosolve multistagedecision-makingproblems.However,itsuffersfromtheinherentcomputationalcomplexity,alsoknownasthe curseofdimensionality [41].Therefore, theneedforapproximativemethodshasbeenrecognizedasearlyasinthelate 1950s[3].In[15],aniterativetechniquecalledpolicyiteration(PI)wasdevised byHowardforMarkovdecisionprocesses(MDPs).Also,Howardreferredtothe iterativemethoddevelopedbyBellman[3,4]asvalueiteration(VI).Computingthe optimalsolutionthroughsuccessiveapproximations,PIiscloselyrelatedtolearning methods.In1968,WerbospointedoutthatPIcanbeemployedtoperformRL[58]. Startingfromthen,manyreal-timeRLmethodsforfindingonlineoptimalcontrol policieshaveemergedandtheyarebroadlycalledapproximate/adaptivedynamic programming(ADP)[31,33,41,43,55,60–65,68],orneurodynamicprogramming [5].ThemainfeatureofADP[59,61]isthatitemploysideasfromRLtoachieve onlineapproximationofthevaluefunction,withoutusingtheknowledgeofthe systemdynamics.

1.1.3TheDevelopmentofADP

ThedevelopmentofADPtheoryconsistsofthreephases.Inthefirstphase,ADP wasextensivelyinvestigatedwithinthecommunitiesofcomputerscienceand

operationsresearch.PIandVIareusuallyemployedastwobasicalgorithms.In [46],Suttonintroducedthetemporaldifferencemethod.In1989,Watkinsproposed thewell-knownQ-learningmethodinhisPhDthesis[56].Q-learningsharessimilarfeatureswiththeaction-dependentheuristicdynamicprogramming(ADHDP) schemeproposedbyWerbosin[62].Otherrelatedresearchworkunderadiscretetime anddiscretestate-spaceMarkovdecisionprocessframeworkcanbefoundin[5,6,8, 9,41,42,48,47]andreferencestherein.Inthesecondphase,stabilityisbroughtinto thecontextofADPwhilereal-timecontrolproblemsarestudiedfordynamicsystems. Tothebestofourknowledge,Lewisandhisco-workersarethefirstwhocontributed totheintegrationofstabilitytheoryandADPtheory[33].Anessentialadvantageof ADPtheoryisthatanoptimalcontrolpolicycanbeobtainedviaarecursivenumericalalgorithmusingonlineinformationwithoutsolvingtheHamilton-Jacobi-Bellman (HJB)equation(fornonlinearsystems)andthealgebraicRiccatiequation(ARE)(for linearsystems),evenwhenthesystemdynamicsarenotpreciselyknown.Related optimalfeedbackcontroldesignsforlinearandnonlineardynamicsystemshavebeen proposedbyseveralresearchersoverthepastfewyears;see,forexample,[7,10,39, 40,50,52,66,69].WhilemostofthepreviousworkonADPtheorywasdevotedto discrete-time(DT)systems(see[31]andreferencestherein),therehasbeenrelatively lessresearchforthecontinuous-time(CT)counterpart.ThisismainlybecauseADPis considerablymoredifficultforCTsystemsthanforDTsystems.Indeed,manyresults developedforDTsystems[35]cannotbeextendedstraightforwardlytoCTsystems. Asaresult,earlyattemptsweremadetoapplyQ-learningforCTsystemsviadiscretizationtechnique[1,11].However,theconvergenceandstabilityanalysisofthese schemesarechallenging.In[40],Murrayet.alproposedanimplementationmethod whichrequiresthemeasurementsofthederivativesofthestatevariables.Assaid previously,Lewisandhisco-workersproposedthefirstsolutiontostabilityanalysis andconvergenceproofsforADP-basedcontrolsystemsbymeansoflinearquadratic regulator(LQR)theory[52].Asynchronouspolicyiterationschemewasalsopresentedin[49].ForCTlinearsystems,thepartialknowledgeofthesystemdynamics (i.e.,theinputmatrix)mustbepreciselyknown.Thisrestrictionhasbeencompletely removedin[18].Anonlinearvariantofthismethodcanbefoundin[22]and[23].

ThethirdphaseinthedevelopmentofADPtheoryisrelatedtoextensionsof previousADPresultstononlinearuncertainsystems.Neuralnetworksandgame theoryareutilizedtoaddressthepresenceofuncertaintyandnonlinearityincontrol systems.See,forexample,[14,31,50,51,57,67,69,70].Animplicitassumptionin thesepapersisthatthesystemorderisknownandthattheuncertaintyisstatic,not dynamic.Thepresenceofdynamicuncertaintyhasnotbeensystematicallyaddressed intheliteratureofADP.Bydynamicuncertainty,werefertothemismatchbetweenthe nominalmodel(alsoreferredtoasthe reduced-ordersystem)andtherealplantwhen theorderofthenominalmodelislowerthantheorderoftherealsystem.Aclosely relatedtopicofresearchishowtoaccountfortheeffectofunseenvariables[60].It isquitecommonthatthefull-stateinformationisoftenmissinginmanyengineering applicationsandonlytheoutputmeasurementorpartial-statemeasurementsare available.AdaptationoftheexistingADPtheorytothispracticalscenarioisimportant yetnon-trivial.Neuralnetworksaresoughtforaddressingthestateestimationproblem

Unknown environment

Valuenetwork

Policynetwork

Action

FIGURE1.2 IllustrationoftheADPscheme.

[12,28].However,thestabilityanalysisoftheestimator/controlleraugmentedsystem isbynomeanseasy,becausethetotalsystemishighlyinterconnectedandoften stronglynonlinear.TheconfigurationofastandardADP-basedcontrolsystemis showninFigure1.2.

Ourrecentwork[17,19,20,21]onthedevelopmentofrobustADP(forshort, RADP)theoryisexactlytargetedataddressingthesechallenges.

1.1.4WhatIsRADP?

RADPisdevelopedtoaddressthepresenceofdynamicuncertaintyinlinearand nonlineardynamicalsystems.SeeFigure1.3foranillustration.Thereareseveral reasonsforwhichwepursueanewframeworkforRADP.Firstandforemost,itis wellknownthatbuildinganexactmathematicalmodelforphysicalsystemsoftenis Action Cost

Unknown environment order

Reducedsystem Dynamic uncertainty

Valuenetwork

Policynetwork

FIGURE1.3 IntheRADPlearningscheme,anewcomponent,knownasdynamicuncertainty,istakenintoconsideration.

ahardtask.Also,eveniftheexactmathematicalmodelcanbeobtainedforsome particularengineeringandbiologicalapplications,simplifiednominalmodelsare oftenmorepreferableforsystemanalysisandcontrolsynthesisthantheoriginal complexsystemmodel.Whilewerefertothemismatchbetweenthesimplified nominalmodelandtheoriginalsystemasdynamicuncertaintyhere,theengineering literatureoftenusesthetermof unmodeleddynamics instead.Second,theobservation errorsmayoftenbecapturedbydynamicuncertainty.Fromtheliteratureofmodern nonlinearcontrol[25,26,30],itisknownthatthepresenceofdynamicuncertainty makesthefeedbackcontrolproblemextremelychallenginginthecontextofnonlinear systems.InordertobroadentheapplicationscopeofADPtheoryinthepresence ofdynamicuncertainty,ourstrategyistointegratetoolsfromnonlinearcontrol theory,suchasLyapunovdesigns,input-to-statestabilitytheory[45],andnonlinear small-gaintechniques[27].ThiswayRADPbecomesapplicabletowideclassesof uncertaindynamicsystemswithincompletestateinformationandunknownsystem order/dynamics.

Additionally,RADPcanbeappliedtolarge-scaledynamicsystemsasshowninour recentpaper[20].Byintegratingasimpleversionofthecyclic-small-gaintheorem [36],asymptoticstabilitycanbeachievedbyassigningappropriateweightingmatrices foreachsubsystem.Further,certainsuboptimalitypropertycanbeobtained.Because ofseveralemergingapplicationsofpracticalimportancesuchassmartelectricgrid, intelligenttransportationsystems,andgroupsofmobileautonomousagents,this topicdeservesfurtherinvestigationsfromanRADPpointofview.Theexistenceof unknownparametersand/ordynamicuncertaintiesandthelimitedinformationof statevariablesgiverisetochallengesforthedecentralizedordistributedcontroller designoflarge-scalesystems.

1.2SUMMARYOFEACHCHAPTER

Thisbookisorganizedasfollows.Chapter2studiesADPforuncertainlinearsystems, ofwhichtheonlyaprioriknowledgeisaninitial,stabilizingstaticstate-feedback controlpolicy.Then,viapolicyiteration,theoptimalcontrolpolicyisapproximated. TwoADPmethods,on-policylearningandoff-policylearning,areintroducedto achieveonlineimplementationofconventionalpolicyiteration.Asaresult,theoptimalcontrolpolicycanbeapproximatedusingonlinemeasurements,insteadofthe knowledgeofthesystemdynamics.

Chapter3furtherextendstheADPmethodsforuncertainaffinenonlinearsystems. Toguaranteeproperapproximationofthevaluefunctionandthecontrolpolicy,neural networksareapplied.ConvergenceandstabilitypropertiesofthenonlinearADP methodarerigorouslyproved.Itisshownthatsemi-globalstabilizationisattainable forageneralclassofcontinuous-timenonlinearsystems,undertheapproximate optimalcontrolpolicy.

Chapter4focusesonthetheoryofglobaladaptivedynamicprogramming(GADP). Itaimsatsimultaneouslyimprovingtheclosed-loopsystemperformanceandachievingglobalasymptoticstabilityoftheoverallsystemattheorigin.Itisshownthat theequalityconstraintusedinpolicyevaluationcanberelaxedtoasum-of-squares

(SOS)constraint.Hence,anSOS-basedpolicyiterationisformulated,byrelaxing theconventionalpolicyiteration.Inthisnewpolicyiterationalgorithm,thecontrol policyobtainedateachiterationstepisgloballystabilizing.Similarly,theSOS-based policyiterationcanbeimplementedonline,withouttheneedtoidentifytheexact systemdynamics.

Chapter5presentsthenewframeworkofRADP.IncontrasttotheADPtheory introducedinChapters2–4,RADPdoesnotrequireallthestatevariablestobe available,northesystemorderassumedknown.Instead,itincorporatesasubsystem knownasthedynamicuncertaintythatinteractswithasimplifiedreduced-order model.WhileADPmethodsareperformedonthereducedmodel,theinteractions betweenthedynamicuncertaintyandthesimplifiedmodelarestudiedusingtools borrowedfrommodernnonlinearsystemanalysisandcontrollerdesign.Thelearning objectiveinRADPistoachieveoptimalperformanceofthereduced-ordermodel intheabsenceofdynamicuncertainties,andmaintainrobustnessofstabilityinthe presenceofthedynamicuncertainty.

Chapter6appliestheRADPframeworktosolvethedecentralizedoptimalcontrol problemforaclassoflarge-scaleuncertainsystems.Inrecentyears,considerable attentionhasbeenpaidtothestabilizationoflarge-scalecomplexsystems,aswell asrelatedconsensusandsynchronizationproblems.Examplesoflarge-scalesystems arisefromecosystems,transportationnetworks,andpowersystems.Often,inrealworldapplications,precisemathematicalmodelsarehardtobuild,andthemodel mismatch,causedbyparametricanddynamicuncertainties,isthusunavoidable. This,togetherwiththeexchangeofonlylocalsysteminformation,makesthedesign problemchallenginginthecontextofcomplexnetworks.Inthischapter,thecontroller designforeachsubsystemonlyneedstoutilizelocalstatemeasurementswithout knowingthesystemdynamics.Byintegratingasimpleversionofthecyclic-smallgaintheorem,asymptoticstabilitycanbeachievedbyassigningappropriatenonlinear gainsforeachsubsystem.

Chapter7studiessensorimotorcontrolwithstaticanddynamicuncertaintiesunder theframeworkofRADP[18,19,21,24].ThelinearversionofRADPisextended forstochasticsystemsbytakingintoaccountsignal-dependentnoise[13],andthe proposedmethodisappliedtostudythesensorimotorcontrolproblemwithboth staticanddynamicuncertainties.Resultspresentedinthischaptersuggestthatthe centralnervoussystem(CNS)mayuseRADP-likelearningstrategytocoordinate movementsandtoachievesuccessfuladaptationinthepresenceofstaticand/or dynamicuncertainties.Intheabsenceofthedynamicuncertainties,thelearning strategyreducestoanADP-likemechanism.

AllthenumericalsimulationsinthisbookaredevelopedusingMATLAB® R2015a.Sourcecodeisavailableonthewebpageofthebook[16].

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CHAPTER2

ADAPTIVEDYNAMICPROGRAMMING FORUNCERTAINLINEARSYSTEMS

Thischapterpresentsareinforcementlearning-inspiredADPapproachforfinding anewclassofonlineadaptiveoptimalcontrollersforuncertainlinearsystems.The onlyinformationrequiredforfeedbackcontrollerdesignisthedimensionofthe statevector x(t ),thedimensionoftheinputvector u(t ),andanapriorilinearstatefeedbackcontrolpolicythatasymptoticallystabilizesthesystemattheorigin.The blockdiagramofsuchasettingisshowninFigure2.1.Theproposedapproach employstheideaofADPtoiterativelysolvethealgebraicRiccatiequation(ARE) usingtheonlineinformationofthestateandtheinput,withoutrequiringtheapriori knowledgeof,oridentifyingthesystemmatrices.

2.1PROBLEMFORMULATIONANDPRELIMINARIES

Consideracontinuous-timelinearsystemdescribedby

= Ax + Bu

where x ∈ ℝn isthesystemstatefullyavailableforfeedbackcontroldesign; u ∈ ℝm isthecontrolinput;and A ∈ ℝn×n and B ∈ ℝn×m areuncertainconstantmatrices. Inaddition,thesystemisassumedtobestabilizable,inthesensethatthereexists aconstantmatrix K ofappropriatedimensionssothat A BK isHurwitz(i.e.,all eigenvaluesof A BK arelocatedintheopen-left-halfplane).

RobustAdaptiveDynamicProgramming,FirstEdition.YuJiangandZhong-PingJiang. ©2017byTheInstituteofElectricalandElectronicsEngineers,Inc.Published2017byJohnWiley&Sons,Inc.

Uncertain linear system

ADP controller x(t) u(t)

FIGURE2.1 ADP-basedonlinelearningcontrolforuncertainlinearsystems.

Weareinterestedinfindingalinearquadraticregulator(LQR)intheformof

whichminimizesthefollowingperformanceindex

where Q = QT ≥ 0, R = RT > 0,with(A, Q1∕2 )observable. AccordingtotheconventionalLQRoptimalcontroltheory,with(2.2),wehave

isafinitematrixifandonlyif A BK isHurwitz.Takingthederivativeof xT Px alongthesolutionof(2.1),itfollowsthat P istheuniquepositivedefinitesolutionto theLyapunovequation

Theoptimalsolutiontotheabove-mentionedproblemisassociatedwiththefollowingwell-knownARE(see[29])

whichhasauniquerealsymmetric,positivedefinitesolution P∗ .Once P∗ isobtained, theoptimalfeedbackgainmatrix K ∗ in(2.2)isthusdeterminedby

Since(2.7)isnonlinearin P,itisusuallydifficulttosolveitanalytically,especially forlarge-sizematrices.Nevertheless,manyefficientalgorithmshavebeendeveloped

tonumericallyapproximatethesolutionto(2.7).Oneofsuchalgorithmsiswidely knownasKleinman’salgorithm[27]andisrecalledbelow.

Theorem2.1.1([27]) LetK0 ∈ ℝm×n beanystabilizingfeedbackgainmatrix(i.e., A BK0 isHurwitz),andrepeatthefollowingstepsfork = 0,1, …

(1) SolvefortherealsymmetricpositivedefinitesolutionPk oftheLyapunov equation

whereAk = A BKk .

(2) Updatethefeedbackgainmatrixby

Then,thefollowingpropertieshold:

(1) A BKk isHurwitz, (2) P∗ ≤ Pk +1 ≤ Pk , (3) lim k

Kk = K

Pk = P

Proof: ConsidertheLyapunovequation(2.9)with k = 0.Since A BK0 isHurwitz, by(2.5)weknow P0 isfiniteandpositivedefinite.Inaddition,by(2.5)and(2.9)we have

Similarly,by(2.5)and(2.7)weobtain

Therefore,wehave P∗ ≤ P1 ≤ P0 .Since P∗ ispositivedefiniteand P0 isfinite, P1 mustbefiniteandpositivedefinite.Thisimpliesthat A BK1 isHurwitz.Repeating theaboveanalysisfor k = 1,2, … provesProperties(1)and(2)inTheorem2.1.1. Finally,since{Pk }isamonotonicallydecreasingsequenceandlowerbounded by P∗ , lim k →∞ Pk = P∞ exists.By(2.9)and(2.10), P = P∞ satisfies(2.7),whichhasa uniquesolution.Therefore, P∞ = P∗ .Theproofisthuscomplete.

ThealgorithmdescribedinTheorem2.1.1isinfactapolicyiterationmethod [17]forcontinuous-timelinearsystems.Indeed,givenastabilizinggainmatrix Kk , (2.9)isknownasthestepof policyevaluation,sinceitevaluatesthecostmatrix Pk

ADAPTIVEDYNAMICPROGRAMMINGFORUNCERTAINLINEARSYSTEMS

associatedwiththecontrolpolicy.Equation(2.10),knownas policyimprovement, findsanewfeedbackgain Kk +1 basedontheevaluatedcostmatrix Pk .

AccordingtoTheorem2.1.1,byiterativelysolvingfor Pk fromtheLyapunov equation(2.9)andupdating Kk accordingto(2.10),the(unique)solutiontothe nonlinearequation(2.7)isnumericallyapproximated.However,ineachiteration, theperfectknowledgeof A and B isrequired,becausethesetwomatricesappear explicitlyin(2.9)and(2.10).InSection2.2,wewillshowhowthispolicyiteration canbeimplementedviareinforcementlearning,withoutknowing A or B,orboth.

2.2ONLINEPOLICYITERATION

Tobeginwith,letusconsiderthefollowingcontrolpolicy

wherethetime-varyingsignal e denotesanartificialnoise,knownasthe exploration noise,addedforthepurposeofonlinelearning.

Remark2.2.1 Choosingtheexplorationnoiseisnotatrivialtaskforgeneral reinforcementlearningproblemsandotherrelatedmachinelearningproblems,especiallyforhigh-dimensionalsystems.Insolvingpracticalproblems,severaltypesof explorationnoisehavebeenadopted,suchasrandomnoise[1,48],exponentially decreasingprobingnoise[41].ForthesimulationsinSection2.4,sumofsinusoidal signalswithdifferentfrequencieswillbeusedtoconstructtheexplorationnoise,as in[22].

Underthecontrolpolicy(2.13),theoriginalsystem(2.1)canberewrittenas

Then,takingthetimederivativeof xT Pk x alongthesolutionsof(2.14),itfollows that

where Qk = Q + K T k RKk . Itisworthpointingoutthat,in(2.15)weused(2.9)toreplacetheterm xT (AT k Pk + Pk Ak )x,whichdependson A and B bytheterm xT Qk x.Thisnewtermcanbe measuredonlinefromreal-timedataalongthesystemtrajectories.Also,by(2.10), wereplacedtheterm BT Pk with RKk +1 ,inwhich Kk +1 istreatedasanotherunknown matrixtobesolvedtogetherwith Pk .Therefore,wehaveremovedthedependencies onthesystemmatrices A and B in(2.15),suchthatitbecomespossibletosolve simultaneouslyfor Pk and Kk +1 usingonlinemeasurements.

Now,byintegratingbothsidesof(2.15)onanygiveninterval[t , t + �� t ]andby rearrangingtheterms,wehave

Wecall(2.16)the onlinepolicyiteration equation,foritreliesontheknowledge ofstatemeasurementsandthecontrolpolicybeingapplied,insteadofthesystem knowledge.Further,wecanuse(2.16)toobtainasetofequations,byspecifying t = tk ,1 , tk ,2 , … , tk ,lk ,with0 ≤ tk ,i + �� t ≤ tk ,i+1 and tk ,i + �� t ≤ tk +1,1 forall k = 0,1, … and i = 1,2, … , lk .Theseequationsbasedoninput/statedatacanthenbeusedtosolve for Pk and Kk +1 .ThedetailswillbegiveninSection2.3. Wealsoconsidertheonlinepolicyiteration(2.16)asan on-policylearning method. Thisisbecauseeachtimewhenanewcontrolpolicy,representedbythegainmatrix Kk ,isobtained,itmustbeimplementedtogeneratenewsolutionsoftheclosed-loop system.Thesenewsolutionsarethenusedforevaluatingthecurrentcostandfinding thenewpolicy.Tobemorespecific, x(t )appearedin(2.16)isthesolutionof(2.14), inwhich Kk isusedtoformulatethecontrolpolicy.

Althoughon-policyiterationcloselymimicsbiologicallearning,theentireadaptationprocesscanbeslowandoneneedstokeepcollectingonlinedatauntilsome convergencecriterionissatisfied.Inengineeringapplications,wearesometimes moreinterestedinobtaininganapproximateoptimalsolutionbymakingfulluse ofsomefinitedata.Thismotivatesustodevelopanoff-policylearningstrategy,in whichweapplyaninitialcontrolpolicytothesystemonafinitenumberoftime intervalsandcollecttheonlinemeasurements.Then,alliterationsareconductedby usingrepeatedlythesameonlinedata.

Tothisend,considerthefollowingsystem,whichistheclosed-loopsystem composedof(2.1)andanarbitraryfeedbackcontrolpolicy

Similarto(2.16),wehave

Although(2.16)and(2.18)shareaverysimilarstructure,afundamentaldifference betweenthemisthat x(t )in(2.18)isgeneratedfromsystem(2.17),inwhich Kk isnot

involved.Therefore,thesameamountofdatacollectedontheinterval[t1 , tl + �� t ]can beusedforcalculating Kk ,with k = 1,2, … Asaresult,wecallthisimplementation off-policylearning,inthattheactualpolicybeenusedcanbeanarbitraryone,as longasitkeepsthesolutionsoftheoverallsystembounded.Theon-policyandthe off-policyimplementationswillbefurtherdiscussedintheSection2.3.

2.3LEARNINGALGORITHMS

2.3.1On-PolicyLearning

Forcomputationalsimplicity,wewouldliketoconverttheunknownmatrices Pk and Kk +1 intoavector.Oneconvenientwaytoachievethisconversion,withoutlosingany information,isviaKroneckerproductrepresentation[28].Oneimportantidentitywe useis

Therefore,wehave

Applyingtheaboveequalities(2.20)–(2.21),(2.16)canbeconvertedto

AsmentionedinSection2.2,wenowcanapply(2.16)onmultipletimeintervals toobtainasetoflinearequationsrepresentedinthefollowingmatrixform

Beforesolvingthepair(Pk , Kk +1 )from(2.23),itisimportanttocheckifthe solutionisunique.Tothisend,Assumption2.3.1isintroduced.

Assumption2.3.1 Foreachk = 0,1,2, …,thereexistsasufficientlylargeinteger lk > 0,suchthatthefollowingrankconditionholds.

Eachinterval[tk ,j , tk ,j+1 ]iscalledasamplinginterval.Weneedtocollectenough sampleddata(whichmeanslargeenough lk foreachiterationstep k ).Thechoiceof theexplorationnoiseplaysavitalrole.Ingeneral,thisrankconditioncanbechecked computationally,butnotanalytically.

Tosatisfytherankconditionin(2.26),agoodpracticeistoassurethatoneiteration stepcanutilizedatafromatleasttwiceasmanysamplingintervalsastheunknowns, thatis, lk ≥ n(n + 1) + 2mn for k = 0,1,2, ⋯.Inaddition,iftheexplorationnoiseis aperiodicalsignal,thelengthofanysamplingintervalshouldbesufficientlylarger thantheperiodofthenoise.

Remark2.3.2 Therankcondition(2.26)introducedaboveisessentiallyinspired fromthepersistentexcitation(PE)conditioninadaptivecontrol[18,33].

Lemma2.3.3 UnderAssumption2.3.1,thereisauniquepair (Pk , Kk +1 ) ∈ ℝn×n × ℝn×m satisfying(2.23)withPk = PT k .

Proof: By(2.24),thereare n(n 1) 2 duplicatedcolumnsinthefirst n2 columnsof Θk . Since Pk issymmetric,thereare n(n 1) 2 duplicatedentriesinthefirst n2 entriesofthe vector [ vec(Pk ) vec(Kk +1 ) ],andtherowindicesoftheseduplicatedentriesmatchexactly theindicesofthe n(n 1) 2 duplicatedcolumnsin Θk .Forexample,if n = 2,thethird columnof Θk istheduplicatedcolumnbecauseitisidenticaltothesecondcolumnof Θk .Meanwhile,thethirdentryin [ vec(Pk ) vec(Kk +1 ) ] isduplicatedfromthesecondentry inthesamevector.

UnderAssumption2.3.1,the n(n+1) 2 + mn distinctcolumnsin Θk arelinearly independent.Asdiscussedabove,theindicesoftheseindependentcolumnsare exactlythesameastherowindicesofthe n(n+1) 2 + mn distinctelementsinthevector

[

vec(Pk )

vec(Kk +1 )

],providedthat Pk issymmetric.Therefore,thepair(Pk , Kk +1 )satisfying (2.23)with Pk = PT k mustbeunique.

Aslongastherankcondition(2.26)issatisfied,theuniquepair(Pk , Kk +1 )mentionedinLemma2.3.3canbeeasilysolvedinMATLAB.Inparticular,thefollowingMATLABfunctioncanbeused.Thefourinputargumentsofthisfunctionare expectedtobe ΘK , Ξk , m,and n,respectively.Thetwooutputargumentsarethe correspondingmatrices Pk and Kk +1 .

function [P,K] = PKsolver(Theta,Xi,m,n)

w = pinv(Theta)∗Xi; %Solveforw = [vec(P);vec(K)]

P = reshape(w(1:n∗n),n,n); %Reshapew(1:nˆ2)togetP

P = (P+P’)/2; %ConvertPtoasymmetricmatrix

K = reshape(w(n∗n+1:end),m,n); %Reshapew(nˆ2+1:end)togetK end

Now,wearereadytogivethefollowingon-policycontinuous-timeADPalgorithm, aflowchartdescribingthealgorithmisshowninFigure2.2.

Start

Initialization: k ← 0; K0 stabilizing; is and t0,1 0=

Online data collection:

Use u = Kk x + e as andpolicy, controlthe compute Θk and Ξk

Policy evaluation and improvement: Solve Pk and Kk+1 from Θk vec(Pk ) vec(Kk+1 ) Ξ= k

Algorithm2.3.4 On-policyADPalgorithm

(1) Initialization:

FindK0 suchthatA BK0 isHurwitz.Letk = 0 andt0,1 = 0

(2) Onlinedatacollection:

Applyu =−Kk x + etothesystemfromt = tk ,1 andconstructeachrowofthe thedatamatrices Θk and Ξk ,untiltherankcondition(2.26)issatisfied.

(3) Policyevaluationandimprovement:

SolveforPk = PT k andKk +1 from(2.23).

(4) Stoppingcriterion:

Terminatetheexplorationnoiseandapplyu =−Kk xasthecontrol,ifk ≥ 1 and

(2.27) with ��> 0 apredefined,sufficientlysmall,threshold.Otherwise,lettk +1,1 satisfytk +1,1 ≥ tk ,lk + �� tandgotoStep(2),withk ← k + 1.

Remark2.3.5 Noticethat ��> 0 isselectedtobalancetheexploration/exploitation trade-off.Inpractice,alarger �� mayleadtoshorterexplorationtimeandtherefore willallowthesystemtoimplementthecontrolpolicyandterminatetheexploration noisesooner.Ontheotherhand,toobtainmoreaccurateapproximationofthe optimalsolution,thethreshold �� ischosensmall,and(2.27)shouldholdforseveral consecutivevaluesofk.Thesameistrueforoff-policyalgorithms.

Theorem2.3.6 LetK0 ∈ ℝm×n beanystabilizingfeedbackgainmatrix,andlet (Pk , Kk +1 ) beapairofmatricesobtainedfromAlgorithm2.3.4.Then,underAssumption2.3.1,thefollowingpropertieshold:

(1) A BKk isHurwitz,

(2) P∗ ≤ Pk +1 ≤ Pk , (3) lim k →∞ Kk = K ∗ , lim k →∞ Pk = P∗ .

Proof: From(2.15),(2.16),and(2.22),oneseesthatthepair(Pk , Kk +1 )obtained from(2.9)and(2.10)mustsatisfy(2.23).Inaddition,byLemma2.3.3,suchapair isunique.Therefore,thesolutionto(2.9)and(2.10)isthesameasthesolutionto (2.23)forall k = 0,1, ….TheproofisthuscompletedbyTheorem2.1.1.

Remark2.3.7 TheADPapproachintroducedhereisrelatedtotheaction-dependent heuristicdynamicprogramming(ADHDP)[46],orQ-learning[44]methodfor discrete-timesystems.Indeed,itcanbeviewedthatwesolveforthefollowingmatrix Hk ateachiterationstep.

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"Where?" said Pooh.

"Anywhere," said Christopher Robin.

So they went off together. But wherever they go, and whatever happens to them on the way, in that enchanted place on the top of the Forest, a little boy and his Bear will always be playing.

BOOKS FOR BOYS AND GIRLS

with Decorations by E. H. SHEPARD:

WHEN WE WERE VERY YOUNG NOW WE ARE SIX WINNIE-THE-POOH

THE HOUSE AT POOH CORNER

THE CHRISTOPHER ROBIN STORY BOOK

SONG-BOOKS FROM THE POEMS OF A. A. MILNE with Music by H. FRASER-SIMSON: FOURTEEN SONGS

THE KING'S BREAKFAST

TEDDY BEAR AND OTHER SONGS

THE HUMS OF POOH

SONGS FROM "NOW WE ARE SIX"

E. P. DUTTON & CO., INC.

*** END OF THE PROJECT GUTENBERG EBOOK THE HOUSE AT POOH

CORNER

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