Abstract
This paper presents a methodology for the actuator fault detection in the satellite’s attitude control system (ACS) by using a dynamic neural network based observer. In this methodology, a neural network is used to model a nonlinear dynamical system. After training, the neural network, it can give very accurate estimation of the attitude positions of the satellite. The difference between the actual and the estimated outputs is used as a residual error for fault detection. The simulation results show advantages of this method as compared to the method based on a generalized Luenberger linear observer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hughes, P.C.: Spacecraft Attitude Dynamics. John Wiley & Sons, Chichester (1986)
Kaplan, M.H.: Modern Spacecraft Dynamics and Control. John Wiley and Sons, Chichester (1976)
Kane, T.R., Likins, P.W., Levinson, D.A.: Spacecraft Dynamics. McGraw-Hill, New York (1983)
Bialke, B.: High Fidelity Mathematical Modeling of Reaction Wheel Performance. Advances in Astronautical Sciences 98, 483–496 (1998)
Singer, S.F.: Torques and Attitude Sensing in Earth Satellites. National Weather Satellite Center, Washington (1964)
Haykin, S.: Neural Networks. A comprehensive Foundation. Prentice-Hall, Englewood Cliffs (1999)
Chen, J., Patton, R.J.: Robust Model-based Fault Diagnosis for Dynamic Systems. Kluwer Int. Series on Asian Studies in Computer & Information Science (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Z., Ma, L., Khorasani, K. (2005). Fault Detection in Reaction Wheel of a Satellite Using Observer-Based Dynamic Neural Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_93
Download citation
DOI: https://doi.org/10.1007/11427469_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)