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The application of self-organising fuzzy control to station-keeping in unmanned air vehicles

Published online by Cambridge University Press:  04 July 2016

J. T. Platts
Affiliation:
Flight Management and Control Department , DERA Bedford, UK
W. Forsythe
Affiliation:
School of Electronic and Electrical Engineering , Loughborough University , UK

Abstract

This paper reports on part of a larger study examining the applicability of fuzzy logic to the higher level supervisory control functions of unmanned air vehicles and details the work so far in testing the feasibility of a candidate self-organising scheme. The candidate algorithm consists of a regular fuzzy control system whose rule set is improved over time as a function of the deviation of the plant from some desired performance. Historically, virtually all work in this area has examined the application of the algorithm to the classic regulator problem, and focussed on achieving both better performance in that role and establishing tuning guidelines based on a more systematic approach. The application discussed in this paper is assessing the algorithm to ascertain its possible utility in the higher level supervisory capacities. The candidate control problem was one of station-keeping of one air vehicle behind another.

Type
Research Article
Copyright
Copyright © Royal Aeronautical Society 2001 

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References

1. Smith, P.R.S., Mayo, E., O'hara, J., and Griffith, D. Combat UAV real-time SEAD mission simulation, 1999, AIAA-99-4185, AIAA Flight Mechanics conference, Baltimore.Google Scholar
2. Noor, A.K. (Compilator), Computational intelligence and its impact on future high-performance engineering systems, 1995, NASA Workshop June 27-28 1995.Google Scholar
3. Sugiyama, K. Analysis and Synthesis of the Rule-Based Self-Organising Controller, 1986, PhD thesis. Queen Mary College, London.Google Scholar
4. Sugiyama, K. Rule-based self-organising controller. In: Gupta, M.M. and Yamakawa, T. (Ed), Fuzzy Computing, 1988, Elsevier Science Publishers, (North Holland).Google Scholar
5. Sutton, R. and Jess, I.M. A design study of a self-organising fuzzy autopilot for ship control, Inst Mech Eng, 1991, 205, pp 3547.Google Scholar
6. Daley, S. and Gill, K.F. A design study of a self-organising fuzzy logic controller, Proc of Inst Mech Engrs, 1986, 200, (C1).Google Scholar
7. Shao, S. Fuzzy self-organising controller and its application for dynamic processes, Fuzzy Sets and Systems 26, 1988, Elsevier Science Publications.Google Scholar
8. Linkens, D.A. and Mahfouf, M. Fuzzy logic knowledge-based control for muscle relaxant anaesthesia, IFAC Modelling and Control in Medicine, 1988, Venice, Italy.Google Scholar
9. Qiao, W.Z., Zhuang, W.P., Heng, T.H. and Shan, S.S. A self-regulating fuzzy controller, Fuzzy Sets and Systems 47, 1992, Elsevier Science Publishers.Google Scholar
10. Nauck, D., Klawonn, F. and Kruse, R. Foundations of Neum-Fuzzy Systems, 1997, John Wiley & Sons.Google Scholar
11. Harris, C.J., Moore, C.G. and Brown, M. Intelligent control: aspects of fuzzy logic and neural nets. World Scientific Series in Robot ics & Automated Systems, 1993, 6.Google Scholar
12. Kosko, B. Fuzzy Engineering, 1997, Prentice Hall.Google Scholar
13. Reznik, L. Fuzzy Controllers, 1997, Newnes.Google Scholar
14. Procyk, T.J. A Self-Organising Controller for Dynamic Processes, 1977, PhD thesis. Queen Mary College, London.Google Scholar
15. Procyk, T.J. and Mamdani, E.H. A linguistic self-organising process controller, Automatica, 1979, 15, pp 1530, Pergammon Press.Google Scholar
16. Yamazaki, T.J. An Improved Algorithm for a Self-Organising Controller, and its Experimental Analysis, 1982, PhD thesis. Queen Mary College, London.Google Scholar
17. Yamazaki, T.J. and Mamdani, E.H. On the performance of a rule-based self-organising controller, 1982, IEEE Conference on Applications of Adaptive & Multivariable Control, Hull.Google Scholar
18. Jantzen, J. The self-organising fuzzy controller, Lecture note 19 Aug 1998 Technical University of Denmark, Dept of Automation, Bldg 326, DK-2800 Lyngby, Denmark, Tech Rep No 98-H 869 (soc), from http://www.iau.dtu.dk. Google Scholar
19. Olivares, M. and Jantzen, J. Fuzzy self-organising control of a pendulum problem, 1999, EUFIT, Crete.Google Scholar
20. Stevens, B.L. and Lewis, F.L. Aircraft Control and Simulation, 1992, John-Wiley & Sons.Google Scholar
21. Griffith, D.V. Pilot thought processes and their input into fuzzy logic controllers, Tech Report CAL/DERA/FUZZY/1/2000, DERA.Google Scholar
22. Smith, P.R.S. and Burnell, J.J. Non-linear dynamic inversion (NDI) A top down approach to control law design, March 1994, DRA/AS/FDS/CR94081/1.Google Scholar
23. Sugeno, M. On stability of fuzzy systems expressed by fuzzy rules with singleton consequences, IEEE Trans on Fuzzy Systems, April 1999, 7.Google Scholar
24. Zak, S.H. Stabilising fuzzy system models using linear controllers, April 1999, IEEE Trans on Fuzzy Systems, 7.Google Scholar
25. Jenkins, D.F. and Pasino, K.M. An introduction to nonlinear analysis of fuzzy control systems, J Intelligent and Fuzzy Systems, 1999, 7.Google Scholar
26. Tang, Y., Zhang, N. and Li, Y. Stable fuzzy adaptive control for a class of nonlinear systems, Fuzzy Sets and Systems, June 1999,104.Google Scholar