Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-28T04:32:43.534Z Has data issue: false hasContentIssue false
  • English
  • Français

A Fuzzy Autopilot Optimized Using a Genetic Algorithm

Published online by Cambridge University Press:  23 November 2009

Robert Sutton  and
Gareth D. Marsden
Gareth D. Marsden
Affiliation:
(Institute of Marine Studies, University of Plymouth)
Get access Rights & Permissions [Opens in a new window]

Extract

The feasibility of using a genetic algorithm to optimize a fuzzy fixed rule based autopilot is considered in this paper. Simulation results are presented to show the applicability of the approach. It is concluded such a procedure gives more credence to the resulting design than can be achieved by totally heuristic methods.

As a result of the general recognition given to the robustness qualities of fuzzy control algorithms, studies have been undertaken in the marine field to develop autopilots based on this approach. Indeed, a fuzzy autopilot was recently installed in a small leisure vessel and very successful fullscale sea trials were undertaken. In the past, the main problem in designing a fuzzy autopilot, or any other fuzzy controller, has been the reliance on purely heuristic methods to obtain an optimum solution. To compensate for such shortcomings in the design cycle analytical approaches are now being proposed.

Keywords

1. Autopilots.2. Fuzzy logic.3. Genetic algorithm.
Type
Research Article
Information
The Journal of Navigation , Volume 50 , Issue 1 , January 1997 , pp. 120 - 131
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

1Polkinghorne, M. N., Roberts, G. N., Burns, R. S. and Winwood, D. (1995). The implementation of fixed rulebase fuzzy logic to the control of small surface ships. Control Engineering Practice, vol. 3, no. 3, pp. 321–328.CrossRefGoogle Scholar
2Sutton, R. and Jess, I. M. (1991). A design study of a self-organizing fuzzy autopilot for ship control. Proc. Instn Mech Engrs, 205, pt I, pp. 35–47.Google Scholar
3Sutton, R., Taylor, S. D. H. and Roberts, G. N. (1996). Neuro-fuzzy techniques applied to a ship autopilot design. This Journal, 49, 410.Google Scholar
4Sutton, R. and Towill, D. R. (1987). Modelling the course-changing control behaviour of a helmsman using fuzzy sets. In Human Decision Making and Control, Patrick, J. & Duncan, K. D (eds.), Elsevier, North-Holland, Amsterdam.Google Scholar
5Caponetto, R., Fortuna, L., Graziani, S. and Xibilia, M. G. (1993). Genetic algorithms and applications in system engineering: a survey. Trans. Inst MC, vol. 15, no. 3, pp. 143–156.CrossRefGoogle Scholar
6Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimisation and Machine Learning Addison-Wesley Publishing Co.Google Scholar
7Freeman, L. M., Kumar, K. K., Karr, C. L. and Meredith, D. L. (1990). Tuning fuzzy logic controllers using genetic algorithms: aerospace applications. Aerospace Applications of Artificial Intelligence Conference, Dayton, OH, USA, Oct., pp. 351–358.Google Scholar
8Karr, C. L. (1991). Design of an adaptive fuzzy logic controller using a genetic algorithm. Proc. 4th International Conference on Genetic Algorithms,San Diego, USA,July, pp. 451–457.Google Scholar
9Pham, D. T. and Karaboga, D. (1991). Optimum design of fuzzy logic controllers using genetic algorithms. Journal of Systems Engineering, vol. 1, pp. 114–118.Google Scholar
10Linkens, D. A. and Nyongesa, H. O. (1995). Genetic algorithms for fuzzy control part 1: off-line system development and application. IEE Proc. Control Theory Appl., vol. 142, no. 3, May, pp. 161–176.CrossRefGoogle Scholar
11Linkens, D. A. and Nyongesa, H. O. (1995). Genetic algorithms for fuzzy control part 2: on-line system development and application. IEE Proc. Control Theory Appl., vol. 142, no. 3, May, pp. 177–185.CrossRefGoogle Scholar
12Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.Google Scholar
13Pedrycz, W. (1993). Fuzzy Control and Fuzzy Systems. Second Edition, Research Studies Press Ltd.Google Scholar
14Witt, N. A. J., Sutton, R. and Miller, K. M. M. (1994). Recent technological advances in the control and guidance of ships, This Journal, 47, 236.Google Scholar
15Chipperfield, A. J., Fonseca, C. M. and Fleming, P. J. (1992). Development of genetic optimization tools for multi-objective optimization problems in CACSD. IEE Colloquium on Genetic Algorithms for Control Systems Engineering, London, May, pp. 3/1–3/6.Google Scholar