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.
State and Parameters Estimation Implementations Based on MHE
State and Parameters Estimation Implementations Based on MHE
Publications
Towards In-Field and Online Calibration of Inertial Navigation Systems Using Moving Horizon Estimation.
Proceedings of the 2019 European Control Conference (ECC).
(2019).
On the Effect of Stabilization Methods for Quaternion Invariants on the Uncertainty in Optimization-based Estimation.
Proceedings of the 9th IFAC Symposium on Robust Control Design ROCOND 2018.
(2018).
On the Effect of Stabilization Methods for Quaternion Invariants on the Uncertainty in Optimization-based Estimation.
IFAC-PapersOnLine.
(2018).
Towards robust sensor fusion for state estimation in airborne applications using GNSS and IMU.
IFAC-PapersOnLine.
(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).
(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.
(2017).
(2017).
An efficient SQP algorithm for Moving Horizon Estimation with Huber penalties and multi-rate measurements.
2016 IEEE Conference on Control Applications (CCA).
(2016).