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A Neural Auto-depth Controller for an Unmanned Underwater Vehicle

Published online by Cambridge University Press:  23 November 2009

R. Sutton ,
C. Johnson  and
G. N. Roberts
R. Sutton
Affiliation:
(Institute of Marine Studies, University of Plymouth)
C. Johnson
Affiliation:
(Institute of Marine Studies, University of Plymouth)
G. N. Roberts
Affiliation:
(University of Wales College, Newport)
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Extract

Artificial neural networks offer an alternative strategy for the nonlinear control of unmanned underwater vehicles (UUVS). This paper investigates the use of a multi-layered perceptron (MLP) network in controlling an UUV over a sea-bed profile and compares the use of applying chemotaxis learning to that of the more commonly employed back propagation algorithm. The results show that, for differing sized MLPs, the chemotaxis algorithm produces a successful controller over the sea-bed profile in an improved training time. Also it will be shown that, in the presence of noise and change in vehicle mass, the neural controller out-performed a classical proportional-integral-derivative controller.

Keywords

1. Depth control.2. Underwater vehicles.3..Artificial intelligence.4. Neural networks.
Type
Research Article
Information
The Journal of Navigation , Volume 50 , Issue 2 , May 1997 , pp. 292 - 302
Copyright
Copyright © The Royal Institute of Navigation 1997

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References

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