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Sensor Fusion / State Estimation for a Kite Power Plant


Fabian Hilti, Roman Patscheider

Semester Thesis, FS12 (10139)

For a successful automated control of a kite, a precise estimation of its position and orientation at all times using GPS and Inertial Measurement Unit (IMU) measurements is essential. In this work we investigated the use of an Extended Kalman Filter that makes use of the information available for the system. To this purpose we developed an algorithm that estimates the state of a spherical pendulum. Using a physical model of this system we were able to reduce the error in the position estimation by 43% compared to an estimator using a conventional "free mass model". Furthermore, the algorithm also works in the absence of a GPS signal while preserving error levels comparable to the "free mass model"-estimator with a GPS signal. These results indicate that the incorporation of a physical model could also improve the state estimation of a kite and reduce its dependency on an accurate GPS signal.


Type of Publication:

(13)Semester/Bachelor Thesis

A. Zgraggen

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% Autogenerated BibTeX entry
@PhdThesis { HilPat:2012:IFA_4262
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