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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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Bibliographic Details
Main Author: Köpf, Florian (auth)
Format: Electronic Book Chapter
Published: KIT Scientific Publishing 2022
Series:Karlsruher Beiträge zur Regelungs- und Steuerungstechnik
Subjects:
Electrical engineering
Adaptive Dynamic Programming (ADP); Reinforcement Learning (RL); Persistent Excitation (PE); adaptive Optimalregelung; lernende Regler; KI; Adaptive Optimal Control; Learning-Based Control; AI
Online Access:DOAB: download the publication
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Summary: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.
Physical Description:1 electronic resource (304 p.)
ISBN:KSP/1000145970
Access:Open Access

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