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Adaptive Optimal Trajectory Tracking Control of Continuous-Time Systems

Bührle, Etienne; Köpf, Florian; Inga, Jairo; Hohmann, Sören


Existing adaptive optimal tracking controllers for linear continuous-time systems rely on a formulation that hinders learning control policies for general reference trajectories; generalizing approaches are currently limited to discrete-time systems. In addition, existing results usually rely on globally discounted objective functions. We demonstrate that global discounting potentially leads to unstable controllers and propose a partially discounted objective function instead, which we show to have a unique, globally asymptotically stabilizing solution in the linear-quadratic case. Based on this result, we present a model-free adaptive tracking control architecture for linear continuous-time systems. Once trained, the controller can be used to track flexible reference trajectories. We demonstrate the functionality of our approach with an example.

DOI: 10.23919/ECC54610.2021.9655005
Zitationen: 1
Zitationen: 1
Zugehörige Institution(en) am KIT Institut für Regelungs- und Steuerungssysteme (IRS)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator KITopen-ID: 1000130140
Erschienen in 19th European Control Conference, June 29 - July 2, 2021, Rotterdam, Virtual Conference
Veranstaltung 19th European Control Conference (ECC 2021), Online, 29.06.2021 – 02.07.2021
Schlagwörter Adaptive Dynamic Programming, Reinforcement Learning, Optimal Control, Adaptive Optimal Trajektory Tracking
Nachgewiesen in Dimensions
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