NDLI: Model updating and thruster fault diagnosis for underwater vehicle

Content Provider	IEEE Xplore Digital Library
Author	Zhenzhong Chu Mingjun Zhang Yujia Wang Weixu Song
Copyright Year	2010
Description	Author affiliation: College of Mechanical and Electrical Engineering, Harbin Engineering University, Heilongjiang, China (Zhenzhong Chu; Mingjun Zhang; Yujia Wang; Weixu Song)
Abstract	As the impact of underwater vehicle dynamics modeling error on fault diagnosis system, a method using improved Elman neural network to modify underwater vehicle dynamics model in the current is proposed. The neural network parameter adjustment law under the Lyapunov stability is given. Sliding mode observer is constructed for state estimation based on the modified dynamics model. The change of state estimation residual of each DOF is analyzed when fault occurs in different thrusters of under vehicle. A thruster fault diagnosis method based on residual state fusion is presented, which has been validated through AUV sea trials data.
Starting Page	1563
Ending Page	1568
File Size	902199
Page Count	6
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5554265
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Vehicle dynamics Vehicles Artificial neural networks Underwater vehicles Observers fusion underwater vehicle fault diagnosis sliding mode observer neural networks
Content Type	Text
Resource Type	Article

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