Hostname: page-component-84b7d79bbc-l82ql Total loading time: 0 Render date: 2024-07-25T21:32:07.120Z Has data issue: false hasContentIssue false
  • English
  • Français

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)
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1Farbrother, H. N. R., and Stacey, B. A., (1993). Aspects of remotely operated vehicle control: A Review. Underwater Technology, vol. 19, no. 1, pp. 24–36.Google Scholar
2Yuh, J., (1990). A Neural Net Controller for Underwater Vehicles. Trans. IEEE Journal of Oceanic Engineering, vol. 13, no. 3, July, pp. 161–66.CrossRefGoogle Scholar
3Farrell, J.Goldenthal, B., and Govingdarajan, K. (1990). Connectionist Learning Control Systems: Submarine Depth Control. Proceedings 29th Conference on Decision and Control, Hawaii, December.Google Scholar
4Bremermann, H. J., and Anderson, R. W., (1989). An Alternative to Back Propagation: A Simple Method for Synoptic Modification for Neural Net Training and Memory. Internal Report, Depart of Maths, Univ. California, Berkeley, USA.Google Scholar
5Beale, R., and Jackson, T., (1990). Neural Computing: An Introduction. Adam Hilger, New York.CrossRefGoogle Scholar
6Daisley, R. E., (1983). Torpedo Equations of Motion. MUSL Document, RED/33516, August.Google Scholar
7Gorman, P., (1987). Three Fin Torpedo Model. MUSL Document, PG/3378, November.Google Scholar
8Koshland, D. E., (1980). Bacterial chemotaxis in relation to neurobiology. Annual Review of Neuroscience, vol. 3, pp. 43–75.CrossRefGoogle ScholarPubMed
9Johnson, C., (1993). Choice of Optimum Network Size for Controlling an uuv. Marine Dynamics Research Group Technical Report MDRG 93009, University of Plymouth, October.Google Scholar
10Ura, T., and Suto, T., (1992). Self-Organizing Neural-Net Controller System for Underwater Vehicle Guidance. Proceedings 2nd IF AC Workshop on Control Application in Marine Systems, Genova, April, pp. 269–277.Google Scholar