State and Parameters Estimation Implementations Based on MHE

State and Parameters Estimation Implementations Based on MHE

Accurate motion tracking and state estimation of a maneuvering kite are crucial for reliable and robust operation of AWE systems. On the other hand, rapid flight maneuvers are still challenging for state-of-the-art estimation algorithms. The objective of this project is to design, analyze, and implement optimization-based moving horizon estimators. The developed moving horizon estimators for sensor fusion using inertial sensors show a more robust estimation behavior. Moreover, the developed algorithms are particularly suited for online-calibration of the employed sensors and system parameters. The additional computational complexity of the algorithm are a challenge for the real-time implementation of moving horizon estimators on embedded systems with restricted computational resources. These are first essential steps towards a full integration of optimization-based estimators and the promising results will influence the design of the next generation of Xsens motion trackers.

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Fabian Girrbach
PhD Researcher

Interested in motion tracking and state estimation.

Publications

(2017). On Robust Sensor Fusion of GNSS and IMU for Airborne Wind Energy Systems. Book of Abstracts of the International Airborne Wind Energy Conference (AWEC 2017).

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(2017). Towards robust sensor fusion for state estimation in airborne applications using GNSS and IMU. Proceedings of the 20th World Congress The International Federation of Automatic Control.

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