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Adaptive Flight Path Control of Airborne Wind Energy Systems

Author

Listed:
  • Tarek N. Dief

    (Research Institute for Applied Mechanics, Kyushu University, 6-1 Kasugakoen, Kasuga, Fukuoka 816-8580, Japan)

  • Uwe Fechner

    (Aenarete–Wind Drones, 2522 DT The Hague, The Netherlands)

  • Roland Schmehl

    (Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands)

  • Shigeo Yoshida

    (Research Institute for Applied Mechanics, Kyushu University, 6-1 Kasugakoen, Kasuga, Fukuoka 816-8580, Japan)

  • Mostafa A. Rushdi

    (Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, 6-1 Kasugakoen, Kasuga, Fukuoka 816-8580, Japan
    Faculty of Engineering and Technology, Future University in Egypt (FUE), 5th Settlement, New Cairo 11835, Egypt)

Abstract

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.

Suggested Citation

  • Tarek N. Dief & Uwe Fechner & Roland Schmehl & Shigeo Yoshida & Mostafa A. Rushdi, 2020. "Adaptive Flight Path Control of Airborne Wind Energy Systems," Energies, MDPI, vol. 13(3), pages 1-29, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:667-:d:316472
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    References listed on IDEAS

    as
    1. van der Vlugt, Rolf & Bley, Anna & Noom, Michael & Schmehl, Roland, 2019. "Quasi-steady model of a pumping kite power system," Renewable Energy, Elsevier, vol. 131(C), pages 83-99.
    2. Cherubini, Antonello & Papini, Andrea & Vertechy, Rocco & Fontana, Marco, 2015. "Airborne Wind Energy Systems: A review of the technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1461-1476.
    3. Fechner, Uwe & van der Vlugt, Rolf & Schreuder, Edwin & Schmehl, Roland, 2015. "Dynamic model of a pumping kite power system," Renewable Energy, Elsevier, vol. 83(C), pages 705-716.
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    Cited by:

    1. Mostafa A. Rushdi & Ahmad A. Rushdi & Tarek N. Dief & Amr M. Halawa & Shigeo Yoshida & Roland Schmehl, 2020. "Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning," Energies, MDPI, vol. 13(9), pages 1-23, May.

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