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Adaptive Dynamic Programming for Model-free Tracking of Trajectories with Time-varying Parameters [in press]

Köpf, Florian; Ramsteiner, Simon; Puccetti, Luca; Flad, Michael; Hohmann, Sören

Recently proposed Adaptive Dynamic Programming (ADP) tracking controllers assume that the reference trajectory follows time-invariant exo-system dynamics—an assumption that does not hold for many applications. In order to overcome this limitation, we propose a new Q-function which explicitly incorporates a parametrized approximation of the reference trajectory. This allows learning to track a general class of trajectories by means of ADP. Once our Q-function has been learned, the associated controller handles time-varying reference trajectories without the need for further training and independent of exo-system dynamics. After proposing this general model-free off-policy tracking method, we provide an analysis of the important special case of linear quadratic tracking. An example demonstrates that our new method successfully learns the optimal tracking controller and outperforms existing approaches in terms of tracking error and cost.

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Verlagsausgabe §
DOI: 10.5445/IR/1000105799
Veröffentlicht am 24.03.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Regelungs- und Steuerungssysteme (IRS)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 0890-6327, 1099-1115
KITopen-ID: 1000105799
Erschienen in International journal of adaptive control and signal processing
Vorab online veröffentlicht am 03.03.2020
Schlagwörter Learning Systems Adaptive Control Intelligent Control Adaptive Dynamic Programming
Nachgewiesen in Web of Science
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