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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. ... mehr

Abstract (englisch):
Model-free control based on the idea of Reinforcement Learning is a promising approach that has recently gained extensive attention. However, Reinforcement-Learning-based control methods solely focus on the regulation problem or learn to track a reference that is generated by a time-invariant exo-system. In the latter case, controllers are only able to track the time-invariant reference dynamics which they have been trained on and need to be re-trained each time the reference dynamics change. Consequently, these methods fail in a number of applications which obviously rely on a trajectory not being generated by an exo-system. ... mehr

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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 Optimal Tracking Reinforcement Learning Optimal Control Adaptive Dynamic Programming
Nachgewiesen in Web of Science
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