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dc.contributor.authorKöpf, Florian
dc.date.accessioned2022-11-14T14:28:27Z
dc.date.available2022-11-14T14:28:27Z
dc.date.issued2022
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/59238
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.en_US
dc.languageGermanen_US
dc.relation.ispartofseriesKarlsruher Beiträge zur Regelungs- und Steuerungstechniken_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineeringen_US
dc.subject.otherAdaptive Dynamic Programming (ADP); Reinforcement Learning (RL); Persistent Excitation (PE); adaptive Optimalregelung; lernende Regler; KI; Adaptive Optimal Control; Learning-Based Control; AIen_US
dc.titleAdaptive Dynamic Programming: Solltrajektorienfolgeregelung und Konvergenzbedingungenen_US
dc.typebook
oapen.identifier.doi10.5445/KSP/1000145970en_US
oapen.relation.isPublishedBy44e29711-8d53-496b-85cc-3d10c9469be9en_US
oapen.series.number18en_US
oapen.pages304en_US
peerreview.anonymityAll identities known
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.review.stagePre-publication
peerreview.review.typeFull text
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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