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Adaptive Dynamic Programming: Solltrajektorienfolgeregelung und Konvergenzbedingungen

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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Enregistré dans:
Auteur principal: Köpf, Florian
Format: Online
Langue:allemand
Publié: KIT Scientific Publishing 2022
Sujets:
Adaptive Dynamic Programming (ADP); Reinforcement Learning (RL); Persistent Excitation (PE); adaptive Optimalregelung; lernende Regler; KI; Adaptive Optimal Control; Learning-Based Control; AI
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering
Accès en ligne:OCN: 1367234043
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Résumé: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.

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