Regular Article - Flowing Matter
Optimizing airborne wind energy with reinforcement learning
The Abdus Salam International Center for Theoretical Physics ICTP, 34151, Trieste, Italy
2 University of Groningen, 9700, Groningen, AB, The Netherlands
3 SISSA International School for Advanced Studies, 34136, Trieste, Italy
4 Laboratoire de physique de l’École normale supérieure (PSL University), CNRS, Sorbonne Université, and Université de Paris, 75005, Paris, France
5 Nonlinear Physical Chemistry Unit, Service de Chimie Physique et Biologie Théorique, Université Libre de Bruxelles, CP 231 - Campus Plaine, 1050, Brussels, Belgium
Accepted: 29 December 2022
Published online: 19 January 2023
Airborne wind energy is a lightweight technology that allows power extraction from the wind using airborne devices such as kites and gliders, where the airfoil orientation can be dynamically controlled in order to maximize performance. The dynamical complexity of turbulent aerodynamics makes this optimization problem unapproachable by conventional methods such as classical control theory, which rely on accurate and tractable analytical models of the dynamical system at hand. Here we propose to attack this problem through reinforcement learning, a technique that—by repeated trial-and-error interactions with the environment—learns to associate observations with profitable actions without requiring prior knowledge of the system. We show that in a simulated environment reinforcement learning finds an efficient way to control a kite so that it can tow a vehicle for long distances. The algorithm we use is based on a small set of intuitive observations and its physically transparent interpretation allows to describe the approximately optimal strategy as a simple list of manoeuvring instructions.
© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.