Fault Detection in Reaction Wheel of a Satellite Using Observer-Based Dynamic Neural Networks | SpringerLink
Skip to main content
Springer Nature Link
Log in

Fault Detection in Reaction Wheel of a Satellite Using Observer-Based Dynamic Neural Networks

  • Conference paper
Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3498))

Included in the following conference series:

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

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Hughes, P.C.: Spacecraft Attitude Dynamics. John Wiley & Sons, Chichester (1986)

    Google Scholar 

  2. Kaplan, M.H.: Modern Spacecraft Dynamics and Control. John Wiley and Sons, Chichester (1976)

    Google Scholar 

  3. Kane, T.R., Likins, P.W., Levinson, D.A.: Spacecraft Dynamics. McGraw-Hill, New York (1983)

    Google Scholar 

  4. Bialke, B.: High Fidelity Mathematical Modeling of Reaction Wheel Performance. Advances in Astronautical Sciences 98, 483–496 (1998)

    Google Scholar 

  5. Singer, S.F.: Torques and Attitude Sensing in Earth Satellites. National Weather Satellite Center, Washington (1964)

    Google Scholar 

  6. Haykin, S.: Neural Networks. A comprehensive Foundation. Prentice-Hall, Englewood Cliffs (1999)

    MATH  Google Scholar 

  7. Chen, J., Patton, R.J.: Robust Model-based Fault Diagnosis for Dynamic Systems. Kluwer Int. Series on Asian Studies in Computer & Information Science (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, H3G 1M8, Canada

    Zhongqi Li, Liying Ma & Khashayar Khorasani

Authors
  1. Zhongqi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liying Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khashayar Khorasani
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

Rights and permissions

Reprints 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)

Publish with us

Policies and ethics

Search

Navigation