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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
Isermann, R.: Fault diagnosis of machines via parameter estimation and knowledge processing–Tutorial paper. Automatica 29, 815–835 (1993)
Visinsky, M., et al.: Expert system framework for fault detection and fault tolerance in robotics. Computers & Electrical Engineering 20, 421–435 (1994)
Qi, J., et al.: Rotorcraft UAV actuator failure estimation with KF-based adaptive UKF algorithm. In: American Control Conference, pp. 1618–1623 (2008)
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)
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)
Schweppe, F.: Recursive state estimation: unknown but bounded errors and system inputs. IEEE Transactions on Automatic Control 13, 22–28 (1968)
Fogel, E., Huang, Y.F.: On the value of information in system identification–Bounded noise case. Automatica 18(2), 229–238 (1982)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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)