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Neuro-Fuzzy Techniques Applied to a Ship Autopilot Design

Published online by Cambridge University Press:  21 October 2009

Robert Sutton ,
Stephen D. H. Taylor  and
Geoffrey N. Roberts
Robert Sutton
Affiliation:
(Institute of Marine Studies, University of Plymouth)
Stephen D. H. Taylor
Affiliation:
(Institute of Marine Studies, University of Plymouth)
Geoffrey N. Roberts
Affiliation:
(Faculty of Technology, Gwent College of Higher Education)
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Abstract

This paper is concerned with an investigation into the use of artificial neural networks in the design of fuzzy autopilots for controlling the yaw dynamics of a modern Royal Navy warship model. Results are presented to show the viability of such an approach and that effective designs can be produced.

Keywords

1. Autopilots.2. Fuzzy logic.3. Artificial neural networks.
Type
Research Article
Information
The Journal of Navigation , Volume 49 , Issue 3 , September 1996 , pp. 410 - 430
Copyright
Copyright © The Royal Institute of Navigation 1996

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References

REFERENCES

1Van Amerongen, J. (1982). Adaptive Steering of Ships: A Model Reference Approach to Improved Manoeuvring and Economical Course-Keeping. Ph.D. thesis, Delft University of Technology.Google Scholar
2Mort, N. and Linkens, D. A. (1980). Self-tuning controllers steering automatic control. Proc. Symposium on Ship Steering Automatic Control, Genova.Google Scholar
3Zuidweg, J. K. (1981). Optimal and sub-optimal feedback in automatic track-keeping system. Proc. 6th Ship Control Systems Symposium, vol. 3, Ottawa.Google Scholar
4Fairbairn, N. A. and Grimble, M. J. (1990). H∞ marine autopilot design for course-keeping and course-changing. Proc. 9th Ship Control Systems Symposium, vol. 3, Bethesda.Google Scholar
5Witt, N. A., Sutton, R. and Miller, K. M. (1994). Recent technological advances in the control and guidance of ships. This Journal 47, 236–258.Google Scholar
6Pedrycz, W. (1993). Fuzzy Control and Fuzzy Systems. Second Edition, Research Studies Press Ltd, Taunton, UK.Google Scholar
7Beale, R. and Jackson, T. (1990). Neural Computing: An Introduction. IOP Publishing Ltd, Bristol, UK.CrossRefGoogle Scholar
8Mort, N. (1983). Autopilot Design for Surface Steering using Self-Tuning Controller Algorithms. Ph.D. thesis, University of Sheffield.Google Scholar
9Clarke, D. W. (1967). Generalized least squares estimation of parameters in a dynamic control model. Proc. IFAC Symposium on Identification in Automatic Control Systems, Prague.Google Scholar
10Comstock, J. P. (1967). Principles of Naval Architecture. SNAME, New York, USA.Google Scholar
11Lloyd, A. R. J. M. (1989). Seakeeping: Ship Behaviour in Rough Weather. Ellis Harwood, UK.Google Scholar
12Ashworth, M. J. (1982). Computer Aided Design of Ship Systems. Ph.D. thesis, University of Wales.Google Scholar
13Sutton, R. (1987). Fuzzy Set Models of the Helmsman Steering a Ship in Course-Keeping and Course-Changing Modes. Ph.D. thesis, University of Wales.Google Scholar
14Pavel, L. and Chelaru, M. (1992). Neural fuzzy architecture for adaptive control. Proceedings of the IEEE International Conference of Fuzzy Systems, 1115–1122.CrossRefGoogle Scholar
15Hayashi, Y., Czogala, E. and Buckley, J. J. (1992). Fuzzy neural controller. Proceedings of the IEEE International Conference on Fuzzy Systems, 197–202.CrossRefGoogle Scholar
16Jang, J.-S. R. (1993). ANFIS: Adaptive–Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3.CrossRefGoogle Scholar
17Jang, J.-S. R. (1994). Self-Learning Fuzzy Controllers Based on Temporal Back Propagation, paper downloaded from the Joint Academic Network, 1994.Google Scholar
18Harris, C. J., Moore, C. G. and Brown, M. (1993). Intelligent Control-Aspects of Fuzzy Logic and Neural Nets. World Scientific Press, UK.CrossRefGoogle Scholar
19Chen, Y.-Y. and Lin, K.-Z. (1992). Learning behaviour of fuzzy controllers with neuron adaptive elements. Proceedings of the 1992 American Control Conference, 1878–1882.CrossRefGoogle Scholar
20Lee, C.-C. (1991). A self-learning rule-based controller employing approximate reasoning and neural net concepts. International Journal of Intelligent Systems, vol. 6, 71–93.CrossRefGoogle Scholar
21Berenji, H. R. and Khedkar, P. (1992). Learning and tuning fuzzy logic controllers through reinforcements. IEEE Transactions on Neural Networks, vol. 3, no. 5, 724–740.CrossRefGoogle ScholarPubMed
22Barto, A. G., Sutton, R. S. and Anderson, C. W. (1983). Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man and Cybernetics, 13, 834–846.CrossRefGoogle Scholar
23Sugeno, M. (ed.) (1985). Industrial Applications of Fuzzy Control. North Holland, The Netherlands.Google Scholar
24Unnikrishnan, K. P. and Venugopal, K. P. (1994). Alopex: A Correlation-Based Learning Algorithm for Feedforward and Recurrent Neural Networks, downloaded from the Joint Academic Network.CrossRefGoogle Scholar
25Koshland, D. E. (1980). Bacterial chemotaxis in relation to neurobiology, Annual Review of Neuroscience, vol. 3, 43–75.CrossRefGoogle ScholarPubMed