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Adaptive Dynamic Programming: Solltrajektorienfolgeregelung und Konvergenzbedingungen
Florian Köpf
2022
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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 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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Keywords
- adaptive dynamic programming (ADP)
- adaptive optimal control
- adaptive Optimalregelung
- AI
- Electrical engineering
- Energy technology & engineering
- KI
- Learning-Based Control
- lernende Regler
- Persistent Excitation (PE)
- reinforcement learning (RL)
- Technology, engineering, agriculture
Links
DOI: 10.5445/KSP/1000145970Editions