In this work, we investigate the benefits and drawbacks of using data-driven models such as artificial neural networks (ANN) in support of spacecraft behaviour modelling process. This approach has been applied to the ESA mission CLUSTER to recover the readings of a simulated failed thermal sensor. The virtual sensor can recover it with an average error of 1,68%. ANNs have been also applied to another ESA mission, ROSETTA. In this case, the objective was to forecast the reading of certain key thermal sensors as a function of Sun distance and attitude, obtaining an average error of 5,5/spl deg/C. This paper discusses the results so far gained. The conclusions include an assessment of the proposed technique and guidelines for cases where it could be beneficial.
Artificial neural networks in support of spacecraft thermal behaviour modelling
2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720) ; 2 ; 1269-1274 Vol.2
2004-01-01
452378 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
4.0801 Artificial Neural Networks in Support of Spacecraft Thermal Behaviour Modelling
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