Predictive Learning Control for Unknown Nonaffine Nonlinear Systems: Theory and Applications PDF
Preview Predictive Learning Control for Unknown Nonaffine Nonlinear Systems: Theory and Applications
Intelligent Control and Learning Systems 8 Qiongxia Yu · Ting Lei · Fengchen Tian · Zhongsheng Hou · Xuhui Bu Predictive Learning Control for Unknown Nonaffine Nonlinear Systems Theory and Applications Intelligent Control and Learning Systems Volume 8 SeriesEditor DongShen ,SchoolofMathematics,RenminUniversityofChina,Beijing, Beijing,China The Springer book series Intelligent Control and Learning Systems addresses the emergingadvancesinintelligentcontrolandlearningsystemsfrombothmathemat- ical theory and engineering application perspectives. It is a series of monographs andcontributedvolumesfocusingonthein-depthexplorationoflearningtheoryin controlsuchasiterativelearning,machinelearning,deeplearning,andotherssharing thelearningconcept,andtheircorrespondingintelligentsystemframeworksinengi- neeringapplications.Thisseriesisfeaturedbythecomprehensiveunderstandingand practicalapplicationoflearningmechanisms.Thisbookseriesinvolvesapplications inindustrialengineering,controlengineering,andmaterialengineering,etc. TheIntelligentControlandLearningSystembookseriespromotestheexchangeof emergingtheoryandtechnologyofintelligentcontrolandlearningsystemsbetween academia and industry. It aims to provide a timely reflection of the advances in intelligent control and learning systems. This book series is distinguished by the combination of the system theory and emerging topics such as machine learning, artificialintelligence,andbigdata.Asacollection,thisbookseriesprovidesvaluable resources to a wide audience in academia, the engineering research community, industry and anyone else looking to expand their knowledge in intelligent control andlearningsystems. · · · Qiongxia Yu Ting Lei Fengchen Tian · Zhongsheng Hou Xuhui Bu Predictive Learning Control for Unknown Nonaffine Nonlinear Systems Theory and Applications QiongxiaYu TingLei HenanKeyLaboratoryofIntelligent SchoolofElectricalandInformation DetectionandControlofCoalMine Engineering Equipment ZhengzhouUniversityofLightIndustry SchoolofElectricalEngineering Zhengzhou,Henan,China andAutomation HenanPolytechnicUniversity ZhongshengHou Jiaozuo,Henan,China SchoolofAutomation AcademyofSystemsScienceandControl FengchenTian QingdaoUniversity HenanKeyLaboratoryofIntelligent Qingdao,Shandong,China DetectionandControlofCoalMine Equipment SchoolofElectricalEngineering andAutomation HenanPolytechnicUniversity Jiaozuo,Henan,China XuhuiBu HenanKeyLaboratoryofIntelligent DetectionandControlofCoalMine Equipment SchoolofElectricalEngineering andAutomation HenanPolytechnicUniversity Jiaozuo,Henan,China ISSN 2662-5458 ISSN 2662-5466 (electronic) IntelligentControlandLearningSystems ISBN 978-981-19-8856-1 ISBN 978-981-19-8857-8 (eBook) https://doi.org/10.1007/978-981-19-8857-8 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SingaporePteLtd.2023 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. 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The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Preface This monograph investigates both theory and applications of predictive learning control(PLC)forunknownandcomplexnonaffinenonlinearsystemsthatoperatein arepetitivepattern.PLCcombinespredictivecontrolintimedomainwithlearning control. Predictive control in time domain can use more future information for controllerdesign,meanwhilecandealwithsystemconstraints.However,itcannot learnfromhistoricalexperienceandtheundesiredtransientbehaviorsintheinitial operationstagecannotbeeliminatednomatterhowmanytimesthesystemrepeats. Learningcontrolcanlearnfromhistoricaloperationprocesses,anditcanalsoachieve perfecttrackingforeachtimepointoverthewholetimeinterval.Byabsorbingthe advantages of these two methods, PLC can not only get an optimal and predictive controlinput,butalsoachieveanimprovedcontrolperformanceandeventualperfect trackingthroughlearning. ThisisthefirstmonographthatfocusesonPLCforunknownnonaffinenonlinear systems.Readersofthismonographwilllearnthedesign,theoreticalanalysis,and practical application of PLC methods without using any mechanism model infor- mation of the system and learn how to cope with various practical problems such assystemconstraints,varyingtriallengths,unknownandtime-varyinginputdelay, availableandunavailablesystemstates,andsoon. Thismonographconsistsoftenchaptersandisdividedintotwoparts.Chapter1 is an introduction to predictive control, learning control, and predictive learning control(PLC).PartIfocusesondesignandtheoreticalanalysisofpredictiveiterative learningcontrol(PILC)whichisthemainandhottopicofPLCandisdividedinto sixchapters. From Chaps. 2 to 7, PILC for unknown nonaffine nonlinear systems, constrainedPILC,PILCwithvaryingtriallengths,PILCwithunknowntimedelay, PILC with full available and unavailable states are designed, respectively. Part II focuses on applications of PILC and predictive repetitive control (PRC) which is anothermaintopicofPLCtopracticalrailwayandroadtransportationsystemsand is divided into three chapters. In Chap. 8, PILC is applied to repeatable automatic high-speed train operation systems. PRC is applied to periodic medium-scale and large-scaleurbantrafficsystemsinChaps.9and10,respectively. v vi Preface The first author would like to thank her doctoral supervisor, Prof. Zhongsheng Hou,QingdaoUniversity,foralltheguidanceandhelphehasgiventhefirstauthor. Moreover,thefirstauthoralsowantstothankherbrothersandsistersinProf.Hou’s teamfortheirsupport.Inaddition,manythankstothemasterstudents,ZhihaoFan andYitengHouofthefirstauthorfortheircarefulproofreadingofthismonograph. Finally,allfiveauthorswouldliketoexpresssincereappreciationtotheirfamilies fortheirunderstandingandlove. TheauthorsgratefullyacknowledgethesupportoftheNationalNaturalScience Foundation of China (Nos. 62003133, 62273133, 61833001); Natural Science Foundation of Henan Province of China (No. 202300410177); Fundamental Research Funds for the Universities of Henan Province (Nos. NSFRF200324, NSFRF210449);KeyScientificResearchProjectsofUniversitiesinHenanProvince (No.20B413002); Research and Practice Project of Higher Education Teaching reform in Henan Province (No.2021SJGLX1011); Innovative Scientists and Tech- nicians Team of Henan Polytechnic University under Grant (No.T2019-2); Inno- vative Scientists and Technicians Team of Henan Provincial High Education (No. 20IRTSTHN019); Doctoral Research Fund of Zhengzhou University of Light Industry(No.2021BSJJ016). Jiaozuo,China QiongxiaYu September2022 Contents 1 Introduction .................................................. 1 1.1 PredictiveControl ........................................ 1 1.2 LearningControl ......................................... 2 1.3 PredictiveLearningControl ................................ 4 1.4 PreviewofThisMonograph ............................... 5 References .................................................... 6 PartI Theory 2 PredictiveIterativeLearningControlforUnknownSystems ...... 11 2.1 Introduction ............................................. 11 2.2 ProblemFormulation ..................................... 12 2.3 PredictiveILCDesign .................................... 14 2.4 SimulationValidation ..................................... 19 2.5 Conclusion .............................................. 21 References .................................................... 22 3 ConstrainedPredictiveIterativeLearningControl ............... 25 3.1 Introduction ............................................. 25 3.2 ProblemFormulation ..................................... 26 3.3 ConstrainedPredictiveILCDesign ......................... 27 3.4 SimulationValidation ..................................... 35 3.5 Conclusion .............................................. 38 References .................................................... 38 4 Predictive Iterative Learning Control for Systems withVaryingTrialLengths ..................................... 41 4.1 Introduction ............................................. 41 4.2 ProblemFormulation ..................................... 42 4.3 DataCompensation-BasedPredictiveILCDesign ............. 44 4.4 SimulationValidation ..................................... 55 vii viii Contents 4.5 Conclusion .............................................. 57 References .................................................... 59 5 Predictive Iterative Learning Control for Systems withUnknownTimeDelay ..................................... 61 5.1 Introduction ............................................. 61 5.2 ProblemFormulation ..................................... 62 5.3 TimeDelayCompensation-BasedPredictiveILCDesign ....... 65 5.4 SimulationValidation ..................................... 79 5.5 Conclusion .............................................. 83 References .................................................... 83 6 PredictiveIterativeLearningControlforSystemswithFull AvailableStates ............................................... 85 6.1 Introduction ............................................. 85 6.2 ProblemFormulation ..................................... 86 6.3 Full-StateObserver-BasedPredictiveILCDesign ............. 88 6.3.1 Full-StateObserverDesign ......................... 88 6.3.2 PredictiveModelConstruction ...................... 92 6.3.3 PredictiveILCDesign ............................. 95 6.4 SimulationValidation ..................................... 99 6.5 Conclusion .............................................. 99 References .................................................... 99 7 Predictive Iterative Learning Control for Systems withUnavailableStates ........................................ 101 7.1 Introduction ............................................. 101 7.2 ProblemFormulation ..................................... 102 7.3 Reduced-OrderObserver-BasedPredictiveILCDesign ........ 104 7.3.1 Reduced-OrderObserverDesign .................... 104 7.3.2 PredictiveModelConstruction ...................... 109 7.3.3 PredictiveILCDesign ............................. 112 7.4 SimulationValidation ..................................... 115 7.5 Conclusion .............................................. 117 References .................................................... 117 PartII Applications 8 High-SpeedTrainAutomaticOperationSystems ................. 121 8.1 Introduction ............................................. 121 8.2 TrainDynamicsandProblemFormation ..................... 122 8.2.1 DynamicsDescriptionofHST ...................... 122 8.2.2 ControlObjective ................................. 123 8.3 RBFNN-BasedPILCDesign ............................... 123 8.4 SimulationValidation ..................................... 129 8.5 Conclusion .............................................. 132 References .................................................... 132 Contents ix 9 Medium-ScaleTwo-RegionUrbanRoadNetworks ............... 133 9.1 Introduction ............................................. 133 9.2 TheStateoftheArtforControlofUrbanRoadNetworks ...... 134 9.2.1 ThePurposeofUrbanRoadTrafficControl ........... 134 9.2.2 TheHistoryandDevelopmentofUrbanRoad TrafficControl ................................... 134 9.2.3 TheClassificationofUrbanRoadTrafficControl ...... 136 9.3 One-Step Model Free Adaptive Predictive Learning PerimeterControl ........................................ 143 9.3.1 TrafficDynamicsforTwo-RegionUrbanTraffic Systems ......................................... 144 9.3.2 Methodology ..................................... 147 9.3.3 NumericalSimulationResults ...................... 150 9.4 Multi-step Model Free Adaptive Predictive Learning PerimeterControl ........................................ 155 9.4.1 Methodology ..................................... 155 9.4.2 NumericalSimulationResults ...................... 159 9.5 Conclusion .............................................. 163 References .................................................... 164 10 Large-ScaleMulti-regionUrbanRoadNetworks ................. 167 10.1 Introduction ............................................. 167 10.2 One-Step Model Free Adaptive Predictive Learning PerimeterControl ........................................ 168 10.2.1 DynamicsfortheLarge-ScaleMulti-regionUrban RoadNetwork .................................... 168 10.2.2 MethodologyFramework .......................... 174 10.2.3 SimulationResults ................................ 180 10.3 Multi-stepModelFreeAdaptiveLearningRouteGuidance andPerimeterControl ..................................... 192 10.3.1 DynamicsModeloftheMRUTS .................... 193 10.3.2 MethodologyFramework .......................... 196 10.3.3 NumericalSimulationResults ...................... 204 10.4 Conclusion .............................................. 215 References .................................................... 215
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