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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.

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), Rotterdam, Niederlande, 29.06.2021 – 02.07.2021
Schlagwörter Adaptive Dynamic Programming Reinforcement Learning Optimal Control Adaptive Optimal Trajektory Tracking
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