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dc.contributor.authorKöpf, Florian
dc.date.accessioned2022-11-15T04:04:22Z
dc.date.available2022-11-15T04:04:22Z
dc.date.issued2022
dc.date.submitted2022-11-14T14:28:27Z
dc.identifierhttps://library.oapen.org/handle/20.500.12657/59238
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/93695
dc.description.abstractIn this work, discrete-time and continuous-time methods that integrate flexible reference trajectory representations into Adaptive Dynamic Programming approaches are presented and analyzed for the first time. Moreover, theoretical conditions on the system state are derived that ensure the persistent excitation property, which is crucial for the convergence of the adaptation. Real-world applications of the presented adaptive optimal trajectory tracking control methods reveal their potential.
dc.languageGerman
dc.relation.ispartofseriesKarlsruher Beiträge zur Regelungs- und Steuerungstechnik
dc.rightsopen access
dc.subject.otherAdaptive Dynamic Programming (ADP); Reinforcement Learning (RL); Persistent Excitation (PE); adaptive Optimalregelung; lernende Regler; KI; Adaptive Optimal Control; Learning-Based Control; AI
dc.subject.otherthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering
dc.titleAdaptive Dynamic Programming: Solltrajektorienfolgeregelung und Konvergenzbedingungen
dc.typebook
oapen.identifier.doi10.5445/KSP/1000145970
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.pages304
dc.seriesnumber18


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