Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-25T05:10:39.322Z Has data issue: false hasContentIssue false
  • English
  • Français

Fuzzy logic approach for estimation of longitudinal aircraft parameters

Published online by Cambridge University Press:  04 July 2016

M. S. Yaacob ,
H. Jamaluddin  and
K. C. Wong
M. S. Yaacob
Affiliation:
Universiti Teknologi Malaysia, Malaysia
H. Jamaluddin
Affiliation:
Universiti Teknologi Malaysia, Malaysia
K. C. Wong
Affiliation:
Department of Aeronautical Engineering, University of Sydney, Australia
Get access Rights & Permissions [Opens in a new window]

Abstract

The use of rule-based fuzzy logic system for estimating the stability and control derivatives for the longitudinal aircraft motion is proposed. The capabilities of the fuzzy logic system in estimating both the short-period and the phugoid mode of motions are explored. The flight data used in the estimation process were generated using the three nonlinear longitudinal equation of motion for a small remotely piloted vehicle with all the aerodynamic coefficients obtained from the wind-tunnel tests. The preferred method of perturbation of the aircraft elevator for data collection is also highlighted. The stability and control derivatives are estimated as the change in the aerodynamic force or moment due to small variation in one of the motion or control variables about its nominal value when the rest of the variables are held constant at their respective nominal values. The changes in the aerodynamic force and moment are predicted using the fuzzy logic system. The results show that the fuzzy logic system has a good potential as alternative tools for parameter estimation from flight data. The proposed method does not require any guesses of the initial values of the flight parameters.

Type
Research Article
Information
The Aeronautical Journal , Volume 106 , Issue 1065 , November 2002 , pp. 585 - 594
Copyright
Copyright © Royal Aeronautical Society 2002 

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. Hamel, P.G. and Jateoaonkar, R.V. Evolution of flight vehicle system identification, J Aircr, 1996, 33, (1), pp 9–28.Google Scholar
2. Hess, R.A. On the use of back propagation with feed-forward neural networks for aerodynamic estimation problem, AIAA paper 93-3638, August 1993.Google Scholar
3. Linse, D.J. and Stengel, R.F. Identification of aerodynamic coefficients using computational neural networks, J Guidance, Control, and Dynamics, 1993, 16, (6), pp 1018–1025.Google Scholar
4. Youssef, H.M. and Juano, J.C. Estimation of aerodynamic coefficients using neural networks, AIAA paper 93-3639, August 1993.Google Scholar
5. Raol, J.R. and Jategaonkar, R.V. Aircraft parameter estimation using recurrent neural networks - a critical appraisal, AIAA paper 95-3504-C, August 1995.Google Scholar
6. Raisinghani, S.C., Ghosh, A.K., and Kalra, P.K. Two new techniques for aircraft parameter estimation using neural networks, Aeronaut J, January 1998, 102, (1011), pp 25–30.Google Scholar
7. Wang, L.-X. and Mendel, J.M. Back-propagation fuzzy system as nonlinear dynamic system identifiers, 1992, International proceedings of the IEEE Conference on Fuzzy Systems, San Diego, March 1992, pp 1409–1418.Google Scholar
8. Newman, D.M. and Wong, K.C. Six degree of freedom flight dynamic and performance simulation of a remotely piloted vehicle, June 1993, Aero Tech Note 9301, Department of Aeronautical Engineering, University of Sydney.Google Scholar
9. Gupta, N.K. and Mehra, R.K. Computational aspects of the maximum likelihood estimation and reduction in sensitivity function calculation, December 1974, IEEE Trans Automatic Control, 19, (6), pp 774–783.Google Scholar
10. Wang, L.-X. Adaptive Fuzzy Systems and Control, Prentice Hall, Englewood Cliffs.Google Scholar
11. Passino, K.M. and Yurkovich, S. Fuzzy Control, 1998, Addison Wesley Longman, Menlo Park, California.Google Scholar
12. Chen, S., Billings, S.A., and Luo, W. Orthogonal least squares learning algorithm for radial basis function networks, Int J Control, 1989, 50, (5), pp 1873–1896.Google Scholar
13. Hong, X. and Billings, S.A. Parameter estimation based on stacked regression and evolutionary algorithms, IEE Proc of Control Theory Appl, 1999, 146, (5), pp 406–414.Google Scholar
14. Ljung, L. and Soderstrom, T. Theory and Practice of Recursive Identification, 1983, MIT Press, Cambridge, MA.Google Scholar
15. Billings, S.A. and Jamaluddin, H. A comparison of the backpropagation and recursive prediction error algorithm for training neural networks, Mech System and Signal Processing, 1991, 5, (3), pp 233–255.Google Scholar
16. Blakelock, J.H. Automatic Control of Aircraft and Missiles, 1991, John Wiley & Sons, New York.Google Scholar
17. Yaacob, M.S. and Jamaluddin, H. Properties of adaptive fuzzy model for system identification of dynamic system using back-propagation algorithm, 2001, Proceedings of First International Conference on Mechatronics - ICOM'01, Kuala Lumpur, Feb 2001, pp 503–513.Google Scholar
18. Yaacob, M.S. and Jamaluddin, H. Effects of user selected conditions on modelling of dynamic system using adaptive fuzzy model, J Teknologi, June 2001, 34, (A), pp 45–60.Google Scholar