Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) |
| Seiten | 114-120 |
| Seitenumfang | 7 |
| ISBN (elektronisch) | 978-1-7281-6422-9 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Extern publiziert | Ja |
| Veranstaltung | 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) - Karlsruhe, Deutschland Dauer: 14 Sept. 2020 → 16 Sept. 2020 |
Abstract
In this contribution a motion estimation approach for the autonomous flight of tethered airfoils is presented. Accurate motion data are essential for the airborne wind energy sector to optimize the harvested wind energy and for the manufacturer of tethered airfoils to optimize the kite design based on measurement data. We propose an estimation based on tether angle measurements from the ground unit and inertial sensor data from the airfoil. In contrast to existing approaches, we account for the issue of tether sag, which renders tether angle measurements temporarily inaccurate. We formulate a Kalman Filter which adaptively shifts the fusion weight to the measurement with the higher certainty. The proposed estimation method is evaluated in simulations, and a proof of concept is given on experimental data, for which the proposed method yields a three times smaller estimation error than a fixed-weight solution.
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2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). 2020. S. 114-120 9235235.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Motion estimation for tethered airfoils with tether sag
AU - Freter, Jan Hendrik
AU - Seel, Thomas
AU - Elfert, Christoph
AU - Göhlich, Dietmar
N1 - Funding Information: We want to specially thank Florian Triebel for implementing and configuring the orientation estimation algorithm on the sensor hardware, which made the sensor fusion possible. Furthermore, our thanks are to Martin Goecks and Enrico Seiler for setting up the micro-controller and IMUs, as well as to Conrad Lange for developing the feedback control algorithm that was used during the experimental trials.
PY - 2020
Y1 - 2020
N2 - In this contribution a motion estimation approach for the autonomous flight of tethered airfoils is presented. Accurate motion data are essential for the airborne wind energy sector to optimize the harvested wind energy and for the manufacturer of tethered airfoils to optimize the kite design based on measurement data. We propose an estimation based on tether angle measurements from the ground unit and inertial sensor data from the airfoil. In contrast to existing approaches, we account for the issue of tether sag, which renders tether angle measurements temporarily inaccurate. We formulate a Kalman Filter which adaptively shifts the fusion weight to the measurement with the higher certainty. The proposed estimation method is evaluated in simulations, and a proof of concept is given on experimental data, for which the proposed method yields a three times smaller estimation error than a fixed-weight solution.
AB - In this contribution a motion estimation approach for the autonomous flight of tethered airfoils is presented. Accurate motion data are essential for the airborne wind energy sector to optimize the harvested wind energy and for the manufacturer of tethered airfoils to optimize the kite design based on measurement data. We propose an estimation based on tether angle measurements from the ground unit and inertial sensor data from the airfoil. In contrast to existing approaches, we account for the issue of tether sag, which renders tether angle measurements temporarily inaccurate. We formulate a Kalman Filter which adaptively shifts the fusion weight to the measurement with the higher certainty. The proposed estimation method is evaluated in simulations, and a proof of concept is given on experimental data, for which the proposed method yields a three times smaller estimation error than a fixed-weight solution.
UR - http://www.scopus.com/inward/record.url?scp=85096100694&partnerID=8YFLogxK
U2 - 10.1109/mfi49285.2020.9235235
DO - 10.1109/mfi49285.2020.9235235
M3 - Conference contribution
SN - 978-1-7281-6423-6
SP - 114
EP - 120
BT - 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
T2 - 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
Y2 - 14 September 2020 through 16 September 2020
ER -