Distributed FDI using Recurrent Neural Networks for Thruster Faults in Formation Flying Satellites

Master thesis (2020)

Authors

M. Henkel Aerospace Engineering

Contributors

J. Guo Space Systems Egineering (mentor)
Faculty
Aerospace Engineering, Aerospace Engineering
Copyright: © 2020 Martin Henkel
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Published Date

17-12-2020

Language

English

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Faculty

Aerospace Engineering

Abstract

Certain Formation Flying missions rely on their orbital control thrusters to maintain the formation, making a Fault Detection and Isolation (FDI) system that is capable of detecting thruster faults very valuable. In addition, communication between satellites in a formation can be expensive. In this thesis, a distributed FDI approach was developed making use of Recurrent Neural Networks (RNNs), utilizing the already available relative position and velocity data as input.
To test the approach, a numerical simulation of a Virtual-Rigid-Body formation in low Earth orbit was developed in MATLAB. The RNNs were trained on noisy data from the simulation, utilizing the tensorflow library. The resulting system was analyzed and compared against a centralized Kalman-filter based approach, in detection capability, isolation capability and robustness. The developed approach showed overall worse performance, but offers reduced communication needs compared to the comparison method.