NDLI: Robust adaptive dynamic surface control for synchronized path following of multiple underactuated autonomous underwater vehicles

Content Provider	IEEE Xplore Digital Library
Author	Wang Hao Wang Dan Peng Zhouhua Yan Langtao Diao Liang
Copyright Year	2014
Description	Author affiliation: Marine Eng. Coll., Dalian Maritime Univ., Dalian, China (Wang Hao; Wang Dan; Peng Zhouhua; Yan Langtao; Diao Liang)
Abstract	In this paper, we consider the problem of synchronized path following for multiple underactuated autonomous underwater vehicles subject to parametric modeling uncertainty and unmodeled dynamics. Nonlinear path following controllers are proposed for individual vehicles that yield convergence of the position tracking errors to a small neighborhood of the origin. Vehicles coordination is reached through synchronizing the along-path speed and path variables, by using a mixture of tools from graph theory and Lyapunov theory. The developed neural network (NN) adaptive technique allows to handle the parametric modeling uncertainty and unmodeled dynamics, without requiring the accurate knowledge of the model. The proposed dynamic surface control (DSC) technique simplifies the synchronized path following controllers by introducing the first-order filters. A complete stability analysis is provided to illustrate that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Simulation results validate the performance and robustness improvement of the proposed strategy.
Starting Page	1949
Ending Page	1954
File Size	204046
Page Count	6
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6896928
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Synchronized Path Following Uncertainty Artificial neural networks Neural Networks Dynamic Surface Control Synchronization Vehicle dynamics Underwater vehicles Vehicles Underactuated Underwater Autonomous Vehicles
Content Type	Text
Resource Type	Article

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