Fault diagnosis for AUVs using support vector machines

被引：23

作者：

Antonelli, G ^{[1
]}

Caccavale, F ^{[1
]}

Sansone, C ^{[1
]}

Villani, L ^{[1
]}

机构：

[1] Univ Cassino, I-03043 Cassino, Italy

来源：

2004 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1- 5, PROCEEDINGS | 2004年

关键词：

fault diagnosis; underwater robotics; neural networks; observers;

D O I：

10.1109/ROBOT.2004.1302424

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：4486 / 4491

页数：6

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[4]

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