Skip to main content

Rotorcraft UAV Actuator Failure Detection Based on a New Adaptive Set-Membership Filter

  • Conference paper
Intelligent Robotics and Applications (ICIRA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7506))

Included in the following conference series:

Abstract

Actuator failure detection method based on a new Adaptive Extended Set-Membership Filter (AESMF) is proposed for Rotorcraft Unmanned Aerial Vehicle (RUAV). The AEMSF proposed in this paper is based on MIT method to optimize the set boundaries of process noises which may be incorrect in modeling or time-variant in operation; estimation stability and boundaries accuracy can be improved compared to the conventional ESMF. Actuator Healthy Coefficients (AHCs) is introduced into the dynamics of RUAV to denote the actuator failure model. Based on AESMF, online estimation of the AHCs can be obtained along with the flight state. With the estimated AHCs, actuator failure can be detected as soon as possible which provide valuable information for fault tolerant control. Efficiency and improvement of this method compared with other online parameters estimation methods is demonstrated by simulation using ServoHeli-20 model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Frank, P.M.: Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results. Automatica 26, 459–474 (1990)

    Article  MATH  Google Scholar 

  2. Isermann, R.: Fault diagnosis of machines via parameter estimation and knowledge processing–Tutorial paper. Automatica 29, 815–835 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  3. Visinsky, M., et al.: Expert system framework for fault detection and fault tolerance in robotics. Computers & Electrical Engineering 20, 421–435 (1994)

    Article  Google Scholar 

  4. Qi, J., et al.: Rotorcraft UAV actuator failure estimation with KF-based adaptive UKF algorithm. In: American Control Conference, pp. 1618–1623 (2008)

    Google Scholar 

  5. Qi, J., Han, J.: Application of wavelets transform to fault detection in rotorcraft UAV sensor failure. Journal of Bionic Engineering 4(4), 265–270 (2007)

    Article  Google Scholar 

  6. Roumeliotis, S.I., et al.: Sensor fault detection and identification in a mobile robot. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 1383–1388 (1998)

    Google Scholar 

  7. Schweppe, F.: Recursive state estimation: unknown but bounded errors and system inputs. IEEE Transactions on Automatic Control 13, 22–28 (1968)

    Article  Google Scholar 

  8. Fogel, E., Huang, Y.F.: On the value of information in system identification–Bounded noise case. Automatica 18(2), 229–238 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  9. Scholte, E., Campbell, M.E.: A nonlinear set‐membership filter for on‐line applications. International Journal of Robust and Nonlinear Control 13, 1337–1358 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  10. Scholte, E., Campbell, M.E.: On-line nonlinear guaranteed estimation with application to a high performance aircraft. In: Proceedings of American Control Conference, pp. 184–190 (2002)

    Google Scholar 

  11. Zhou, B., et al.: A UD factorization-based nonlinear adaptive set-membership filter for ellipsoidal estimation. International Journal of Robust and Nonlinear Control 18, 1513–1531 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  12. Koo, T., Sastry, S.: Output tracking control design of a helicopter model based on approximated linearization. In: Proceedings of the 37th IEEE conference on Decision & Control, Tampa, Florida, USA, pp. 3625–3640 (December 1998)

    Google Scholar 

  13. Song, D., Wu, C., Qi, J., Han, J.: A MIT-based nonlinear adaptive set-membership filter for ellipsoidal estimation. Acta Automatica Sinica (to be published)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wu, C., Song, D., Qi, J., Han, J. (2012). Rotorcraft UAV Actuator Failure Detection Based on a New Adaptive Set-Membership Filter. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33509-9_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33509-9_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33508-2

  • Online ISBN: 978-3-642-33509-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics