Authors:
Lilian Ronceray
1
;
Matthieu Jeanneau
1
;
Daniel Alazard
2
;
Philippe Mouyon
3
and
Sihem Tebbani
4
Affiliations:
1
Airbus France, France
;
2
Supaéro, France
;
3
ONERA, France
;
4
École Supérieure d’Électricité, France
Keyword(s):
Local learning, radial-basis neural networks, real-time parameter estimation.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
Abstract:
This paper proposes an approach based upon local learning techniques and real-time parameter estimation, to tune an aircraft sideslip estimator using radial-basis neural networks, during a flight test. After a presentation of the context, we recall the local model approach to radial-basis networks. The application to the estimation of the sideslip angle of an aircraft, is then described and the various results and analyses are detailled at the end before suggesting some improvement directions.