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Open AccessArticle

Adaptive Flight Path Control of Airborne Wind Energy Systems

1
Research Institute for Applied Mechanics, Kyushu University, 6-1 Kasugakoen, Kasuga, Fukuoka 816-8580, Japan
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Aenarete–Wind Drones, 2522 DT The Hague, The Netherlands
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Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands
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Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, 6-1 Kasugakoen, Kasuga, Fukuoka 816-8580, Japan
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Faculty of Engineering and Technology, Future University in Egypt (FUE), 5th Settlement, New Cairo 11835, Egypt
*
Author to whom correspondence should be addressed.
Energies 2020, 13(3), 667; https://doi.org/10.3390/en13030667
Received: 26 November 2019 / Revised: 10 January 2020 / Accepted: 21 January 2020 / Published: 4 February 2020
(This article belongs to the Section Wind, Wave and Tidal Energy)
In this paper, we applied a system identification algorithm and an adaptive controller to a simple kite system model to simulate crosswind flight maneuvers for airborne wind energy harvesting. The purpose of the system identification algorithm was to handle uncertainties related to a fluctuating wind speed and shape deformations of the tethered membrane wing. Using a pole placement controller, we determined the required locations of the closed-loop poles and enforced them by adapting the control gains in real time. We compared the path-following performance of the proposed approach with a classical proportional-integral-derivative (PID) controller using the same system model. The capability of the system identification algorithm to recognize sudden changes in the dynamic model or the wind conditions, and the ability of the controller to stabilize the system in the presence of such changes were confirmed. Furthermore, the system identification algorithm was used to determine the parameters of a kite with variable-length tether on the basis of data that were recorded during a physical flight test of a 20 kW kite power system. The system identification algorithm was executed in real time, and significant changes were observed in the parameters of the dynamic model, which strongly affect the resulting response. View Full-Text
Keywords: airborne wind energy; kite power system; system identification; adaptive algorithms; pole placement airborne wind energy; kite power system; system identification; adaptive algorithms; pole placement
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MDPI and ACS Style

Dief, T.N.; Fechner, U.; Schmehl, R.; Yoshida, S.; Rushdi, M.A. Adaptive Flight Path Control of Airborne Wind Energy Systems. Energies 2020, 13, 667.

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