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Adaptive Dynamic Programming: Solltrajektorienfolgeregelung und Konvergenzbedingungen

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In 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...

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Tallennettuna:
Bibliografiset tiedot
Päätekijä: Köpf, Florian (auth)
Aineistotyyppi: Elektroninen Kirjan osa
Julkaistu: KIT Scientific Publishing 2022
Sarja:Karlsruher Beiträge zur Regelungs- und Steuerungstechnik 18
Aiheet:
Electrical engineering
Adaptive Dynamic Programming (ADP); Reinforcement Learning (RL); Persistent Excitation (PE); adaptive Optimalregelung; lernende Regler; KI; Adaptive Optimal Control; Learning-Based Control; AI
Linkit:OAPEN Library: download the publication
OAPEN Library: description of the publication
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Yhteenveto:In 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.
Ulkoasu:1 electronic resource (304 p.)
ISBN:KSP/1000145970
Pääsy:Open Access

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