By Topic

Fault diagnosis for AUVs using support vector machines

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Antonelli, G. ; Universita di Cassino, Italy ; Caccavale, F. ; Sansone, C. ; Villani, L.

In this paper an observer-based fault diagnosis (FD) approach for autonomous underwater vehicles (AUVs), subject to actuator faults (i.e., faults affecting the propulsion system and/or the control surfaces), is proposed. A diagnostic observer is developed based on the available dynamic model of the AUV. Compensation of unknown dynamics, uncertainties and disturbances is achieved through the adoption of a class of neural interpolators (support vector machines, SVMs) trained off line. On the other hand, interpolation of unknown actuator faults is performed by adopting a radial basis function (RBF) network, whose weights are adaptively tuned on line. The effectiveness of the approach is tested in a simulation case study developed for the NPS AUV II (PHOENIX) vehicle.

Published in:

Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 IEEE International Conference on  (Volume:5 )

Date of Conference:

26 April-1 May 2004