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Proceedings Paper

Online adaptive and neural network control of underwater vehicles
Author(s): Myung-Hyun Kim; Daniel J. Inman
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Paper Abstract

An adaptive neural network controller has been developed for a model of an underwater vehicle. This controller combines radial basis neural network and sliding mode control techniques. No prior off-line training phase is required and this scheme exploits the advantages of both neural network control and sliding mode control. An on-line stable adaptive law is derived using Lyapunov theory. It is observed that the number of neurons and the width of Gaussian function should be chosen carefully. Performance of the controller is demonstrated by computer simulations.

Paper Details

Date Published: 15 November 1999
PDF: 11 pages
Proc. SPIE 3838, Mobile Robots XIV, (15 November 1999); doi: 10.1117/12.369261
Show Author Affiliations
Myung-Hyun Kim, Virginia Polytechnic Institute and State Univ. (United States)
Daniel J. Inman, Virginia Polytechnic Institute and State Univ. (United States)

Published in SPIE Proceedings Vol. 3838:
Mobile Robots XIV
Douglas W. Gage; Howie M. Choset, Editor(s)

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