Data-Driven Modeling, Filtering and Control: Methods and applications

301 Pages·2019·42.12 MB·English

by  Carlo Novara, Simone Formentin

IETCONTROL,ROBOTICSANDSENSORSSERIES123
Data-Driven Modeling,
Filtering and Control
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Data-Driven Modeling,
Filtering and Control
Methods and applications
Edited by
Carlo Novara and Simone Formentin
TheInstitutionofEngineeringandTechnology
PublishedbyTheInstitutionofEngineeringandTechnology,London,UnitedKingdom
TheInstitutionofEngineeringandTechnologyisregisteredasaCharityinEngland&Wales
(no.211014)andScotland(no.SC038698).
©TheInstitutionofEngineeringandTechnology2019
Firstpublished2019
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Contents
1 Introduction 1
SimoneFormentinandCarloNovara
1.1 Introduction 1
1.2 State-of-the-art 4
1.3 Goalsandstructureofthebook 8
References 10
PARTI Data-drivenmodeling 15
2 Akernel-basedapproachtosupervisednonparametric
identificationofWienersystems 17
FeiXiong,YongfangCheng,OctaviaCamps,MarioSznaier,and
ConstantinoLagoa
2.1 Introductionandmotivation 17
2.2 Preliminaries 19
2.2.1 Notationanddefinitions 20
2.2.2 Solvingpolynomialoptimizationproblemsviaconvex
optimization 20
2.2.3 Exploitingsparsityinpolynomialoptimization 21
2.3 Problemstatement 22
2.4 MaximummarginHankelclassifiers 24
2.4.1 Furthercomputationalcomplexityreduction 25
2.4.2 Exploitingsparsity 27
2.5 Examples 28
2.5.1 Syntheticdata 28
2.5.2 Application:activityrecognitionfromvideodata 32
2.6 Conclusions 32
Acknowledgments 33
References 33
3 Identificationofaquasi-LPVmodelforwing-flutter
analysisusingmachine-learningtechniques 37
RodrigoAlviteRomano,MarceloMendesLafetáLima,
PauloLopesdosSantos,andTeresaPaulaAzevedoPerdicoúlis
3.1 Introduction 37
3.2 LPVstate-spacemodelparameterization 39
vi Data-drivenmodeling,filteringandcontrol
3.3 Modelestimation 40
3.3.1 Parameterreconstruction 43
3.4 Ensembleestimationapproach 44
3.5 Wing-fluttermodelidentification 47
3.6 Concludingremarks 53
References 53
4 Experimentalmodelingofaweb-windingmachine:
LPVapproaches 57
JoseVuelvas,FredyRuiz,andCarloNovara
4.1 Introduction 57
4.2 Sparsesetmembershipidentificationofstate-spaceLPVsystems 59
4.3 Interpolatedidentificationofstate-spaceLPVsystems 62
4.4 Web-windingsystemidentification 64
4.4.1 Theweb-windingsystem 64
4.4.2 Experimentdescription 66
4.4.3 SparsesetmembershipLPVmodel 67
4.4.4 InterpolatedLPVmodel 68
4.4.5 Modelvalidationandresults 68
4.5 Conclusion 70
References 71
5 Insituidentificationofelectrochemicalimpedancespectra
forLi-ionbatteries 75
TyroneVincent,PeterJ.Weddle,AlekseiLaRue,andRobertJ.Kee
5.1 Introduction 75
5.1.1 Motivation:understandingbatterydynamics 75
5.1.2 TraditionalmethodsformeasuringEIS 77
5.1.3 Relatedwork 78
5.1.4 Outlineofapproach 78
5.2 Method 79
5.2.1 Datacollection 79
5.2.2 Identification 82
5.2.3 Frequencyresponseanduncertaintyestimation 83
5.2.4 Combinedfrequencyresponseestimate 87
5.2.5 Reviewoffrequencyidentificationmethod 88
5.3 Exampleexperimentalresults 89
5.3.1 ExperimentalconditionsforPRBSperturbation 89
5.3.2 Experimentalconditionsforsinusoidalperturbation 89
5.3.3 Results 89
Acknowledgments 91
References 91
Contents vii
PARTII Data-drivenfilteringandcontrol 95
6 Dynamicmeasurement 97
IvanMarkovsky
6.1 Introduction 97
6.1.1 Literaturereview 98
6.2 Problemsetup 99
6.3 Model-basedvsdata-drivenapproaches 100
6.4 Maximum-likelihooddata-drivenestimationmethod 101
6.5 Examples 102
6.5.1 Methodsandevaluationcriterion 102
6.5.2 Exampleoftemperaturemeasurement 103
6.5.3 Exampleofmassmeasurement 104
6.5.4 Results 104
6.6 Conclusionsanddiscussion 106
Acknowledgments 106
References 107
7 Multivariableiterativelearningcontrol:analysisand
designsforengineeringapplications 109
LennartBlanken,JurgenvanZundert,RobindeRozario,
NardStrijbosch,andTomOomen
7.1 Introduction 109
7.1.1 ILCforcomplexengineeringapplications 109
7.1.2 Designrequirementsforhigh-precisionapplications 110
7.1.3 RobustmultivariableILCdesign:theimportanceof
(under)modeling(R1–R2) 110
7.1.4 Model-freeiterativelearning(R2) 111
7.1.5 ILCforvaryingtasks(R3) 112
7.1.6 Contributions 112
7.1.7 Notation 113
7.2 Systemdescriptionandproblemformulation 113
7.2.1 ILCframework 113
7.2.2 Convergenceandperformance 114
7.2.3 Designconditionsforconvergenceand
performance 115
7.2.4 Modelingconsiderations 116
7.3 ILCdesign—theSISOcase 116
7.3.1 Manualdesigninthefrequencydomain 117
7.3.2 Designoflearningfilter:SISOinversion
techniques 119
7.3.3 TowardMIMOILCdesign:naiveSISOdesign
forMIMOsystems 122
7.4 ILCDesign—theMIMOcase 123
viii Data-drivenmodeling,filteringandcontrol
7.4.1 Interactionanalysis 127
7.4.2 Decouplingtransformations 128
7.4.3 Robustmulti-loopSISOdesign 128
7.4.4 RobustdecentralizedMIMOdesign 128
7.4.5 CentralizedMIMOdesign 130
7.5 Iterativeinversion-basedcontrol:avoidingtheneed
forparametricmodels 132
7.5.1 Systemdescriptionandprocedure 132
7.5.2 Convergenceanalysis,modelingrequirementsanddesign 133
7.6 ILCwithbasisfunctions:enhancingflexibilitytovaryingtasks 134
7.6.1 FlexibilityinILC—casestudyonaflatbedprinter 134
7.6.2 BasisfunctionsinILC 135
7.6.3 Projection-basedMIMOILCwithbasisfunctions:
frequency-domaindesign 136
7.7 Conclusionandongoingwork 137
Acknowledgments 138
References 138
8 Algorithmsfordata-drivenH∞-normestimation 145
CristianR.RojasandMatiasI.Müller
8.1 Motivationandproblemformulation 145
8.1.1 Problemformulation 147
8.2 Poweriterations 148
8.2.1 Poweriterationsinlinearalgebra 148
8.2.2 Poweriterationsforlineardynamicalsystems 149
8.2.3 Anexample 150
8.3 Multi-armedbandits 152
8.3.1 Stochasticmulti-armbanditsinanutshell 152
8.3.2 H∞–normestimationasanMABproblem 153
8.3.3 Regretlowerboundsandoptimalalgorithms 155
8.3.4 TheweightedThompsonsampling(WTS)algorithm 156
8.3.5 Anillustrativeexample 158
8.4 Extensionstononlinearsystems 159
8.4.1 deBruijngraphsandprimecycles 160
8.4.2 Findingtheoptimalstationarysequence 161
8.5 Discussionandextensions 162
References 162
9 AcomparativestudyofVRFTandset-membershipdata-driven
controllerdesigntechniques:activesuspensiontuningcase 165
FreddyValderramaandFredyRuiz
9.1 Introduction 165
9.2 Problemstatement 166
9.3 Controllertuningfromdata 168
Contents ix
9.3.1 Set-membershipapproach 168
9.3.2 TuningviaVRFT 172
9.4 Activesuspensiontuningcasestudy 174
9.4.1 Controllertuningproblem 175
9.4.2 MonteCarloexperiment 177
9.4.3 Processdisturbanceexperiment 183
9.5 Conclusions 184
Acknowledgment 186
References 186
10 Relativeaccuracyoftwomethodsforapproximatingobserved
fisherinformation 189
ShenghanGuoandJamesC.Spall
10.1 Introduction 189
10.2 Background 192
10.2.1 TheCentralLimitTheorem 192
10.2.2 Taylorexpansion(Taylorseries) 193
10.3 Theoreticalanalysis 193
10.4 Numericalstudies 201
10.5 Conclusionsandfuturework 206
10.5.1 Conclusion 206
10.5.2 Futurework 207
References 211
11 Ahierarchicalapproachtodata-drivenLPVcontrol
designofconstrainedsystems 213
DarioPiga,SimoneFormentin,RolandToth,
AlbertoBemporad,andSergioMatteoSavaresi
11.1 Introduction 213
11.2 Relatedworks 215
11.3 Problemformulation 215
11.4 Ahierarchicalapproach 217
11.5 Data-driveninnercontrollerdesign 218
11.5.1 Inversionofthereferencemodel 221
11.5.2 Data-drivencontrollerdesign 222
11.5.3 Dualproblem 225
11.6 Outercontrollerdesign 227
11.7 Casestudy:servo-positioningsystem 229
11.7.1 Systemdescription 230
11.7.2 Desiredinnerclosed-loopbehavior 231
11.7.3 Innercontrollerdesign 231
11.7.4 Achievedinnerclosed-loopbehavior 232
11.7.5 Outercontrollerdesign 233
11.8 Conclusions 234
References 235