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Sensor Fusion for Airborne Wind Energy Estimation


A. Millane

Master Thesis, FS14 (10364)

In this thesis estimation approaches are presented for an autonomous tethered kite system for the purpose of airborne wind energy generation. Accurate estimation of the kite state is critical to the performance of the automatic flight controller. We propose estimation schemes which fuse measurements from tether sensing, range sensing, based on Ultra- Wideband radios, and inertial readings from an Inertial Measurement Unit. The kite process is modelled using sensor-driven kinematics, driven by inertial sensors which are affected by time-varying biases. Estimates are computed using a Multiplicative Extended Kalman Filter scheme, typically used for estimation problems involving orientation quaternions. This approach is combined with a Hybrid Extended Kalman Filter, a technique used to compute estimates of non-linear systems with continuous-time process dynamics and discrete-time measurements. We perform a non-linear observability analysis of the suggested estimation schemes, based on the observability rank condition. Finally, we present results of implementing the estimation schemes within simulation models of an Airborne Wind Energy system and compare performance to an existing estimation scheme.

Supervisors: Henrik Hesse, Tony Wood, Roy Smith


Type of Publication:

(12)Diploma/Master Thesis

H. Hesse

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2015:IFA_5149
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