Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-22T10:17:17.492Z Has data issue: false hasContentIssue false
  • English
  • Français

Fuzzy modelling for aircraft dynamics identification

Published online by Cambridge University Press:  04 July 2016

G. Mengall
G. Mengall*
Affiliation:
Università di Pisa, Dipartimento di Ingegneria Aerospaziale, Pisa, Italy
Get access Rights & Permissions [Opens in a new window]

Abstract

A new methodology is described to identify aircraft dynamics and extract the corresponding aerodynamic coefficients. The proposed approach makes use of fuzzy modelling for the identification process where input/output data are first classified by means of the concept of fuzzy clustering and then the linguistic rules are extracted from the fuzzy clusters. The fuzzy rule-based models are in the form of affine Takagi-Sugeno models, that are able to approximate a large class of nonlinear systems. A comparative study is performed with existing techniques based on the employment of neural networks, showing interesting advantages of the proposed methodology both for the physical insight of the identified model and the simplicity to obtain accurate results with fewer parameters to be properly tuned.

Type
Research Article
Information
The Aeronautical Journal , Volume 105 , Issue 1051 , September 2001 , pp. 551 - 555
Copyright
Copyright © Royal Aeronautical Society 2001 

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

1. Hess, R.A. On the use of back propagation with feed-forward neural networks for the aerodynamic estimation problem, AIAA Paper 93-3638, August 1993.Google Scholar
2. Rokhsaz, K. and Steck, J.E. Use of neural networks in control of high-alpha manouvers, J Guidance, Control and Dynamics, September-October 1993, 16, (5), pp 934–939.Google Scholar
3. Raol, J.R. and Jategaonkar, R.V. Aircraft parameter estimation using recurrent neural networks — a critical appraisal, AIAA Paper 95-3504, August 1995.Google Scholar
4. Hamel, P.G. and Jategaonkar, R.V. Evolution of flight vehicle system identification, J Aircr, January 1996, 33, (1), pp 9–28.Google Scholar
5. Raisingani, S.C., Ghosh, A.K. and Kaira, P.K. Two new techniques for aircraft parameter estimation using neural networks, Aeronaut J, January 1998, 102, (1011), pp 25–30.Google Scholar
6. Iliff, K.W. Parameter estimation for flight vehicles, J Aircr, May 1989, 12, (5), pp 609–622.Google Scholar
7. Zadeh, L.A. Fuzzy sets, Information and Control, 1965, 8, pp 338–353.Google Scholar
8. Zadeh, L.A. Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man and Cybernetics, January 1973, volume SMC-3, (1), pp 28–44 Google Scholar
9. Driankov, D., Hellendoorn, H. and Reinfrank, M. An Introduction to Fuzzy Control, Springer-Verlag, Berlin, 1993.Google Scholar
10. Zimmermann, H.J. Fuzzy Set Theory and its Application, Kluwer Academic Publishers, Boston, 1996.Google Scholar
11. Bezdek, J.C. Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.Google Scholar
12. Gustafson, D.E. and Kessel, W.C. Fuzzy clustering with a fuzzy covariance matrix, Proceedings of IEEE CDC, San Diego, California, USA, 1979, pp 761–766.Google Scholar
13. Krishnapuram, R. and Freg, C.P. Fitting an unknown number of lines and planes to image data through compatible cluster merging, Pattern Recognition, 1992, 25, (4), pp 385–400.Google Scholar
14. Babuska, R. and Verbruggen, H. New approach to constructing fuzzy relational models from data, Proceedings Third European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, 1995, pp 583–587.Google Scholar
15. Babuska, R. Fuzzy Modelling for Control, Kluwer Academic Publishers, Boston, 1998.Google Scholar
16. Takagi, T. and Sugeno, M. Fuzzy identification of systems and its application to modelling and control, IEEE Transactions on Systems, Man and Cybernetics, 1985, 15, (1), pp 116–132.Google Scholar
17. Klein, V. Estimation of aircraft aerodynamic parameters from flight data, Progress in Aerospace Sciences, Pergamon, Oxford, May 1989, 26, pp 1–77.Google Scholar
18. Wang, L.X. Adaptive Fuzzy Systems and Control, Design and Stability Analysis,Prentice Hall, New Jersey, 1994.Google Scholar