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Particle Filter for Fault Diagnosis and Ropbust Navigation of Underwater Robot

Zhao, Bo; Skjetne, Roger; Blanke, Mogens; Dukan, Fredrik
Conference object, Journal article, Peer reviewed
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Zhao_Skjetne_Blanke_Dukan_2014_epublished.pdf (2.319Mb)
URI
http://hdl.handle.net/11250/2393363
Date
2014
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Original version
IEEE Transactions on Control Systems Technology 2014   10.1109/TCST.2014.2300815
Abstract
A particle filter (PF)-based robust navigation with

fault diagnosis (FD) is designed for an underwater robot, where

10 failure modes of sensors and thrusters are considered. The

nominal underwater robot and its anomaly are described by a

switching-mode hidden Markov model. By extensively running

a PF on the model, the FD and robust navigation are achieved.

Closed-loop full-scale experimental results show that the proposed

method is robust, can diagnose faults effectively, and can

provide good state estimation even in cases where multiple faults

occur. Comparing with other methods, the proposed method can

diagnose all faults within a single structure, it can diagnose

simultaneous faults, and it is easily implemented.
Description
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Publisher
IEEE

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