Model-based fault diagnosis and fault-tolerant data fusion algorithms for the ESA’s In-Orbit-Assembly project

This paper is an application oriented paper. It aims at demonstrating how model–based fault diagnosis and tolerant theories can be used to accommodate faults that may occur in spacecraft navigation units, i.e. at sensor level. The application support is the ESA’s In Orbit Assembly (IOA) project. This project is undertaken with GMV Space and Thales Alenia Space industries. The goal of the IOA mission is to assembly autonomously a telescope on a halo orbit around the Earth-Moon L2 point. The proposed solution is based on the observer structure of the (discrete time) subspace predictor and a nonlinear observer, for fault diagnosis. For fault tolerance, the solution relies on multi-sensor data fusion techniques based on the extended Kalman filter updated from the inverse-covariance form of the Kalman filter. The solution is evaluated under a realistic industrial environment.