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New helicopter model identification method based on flight test data

  • S. De Jesus Mota and R. M. Botez (a1)

Abstract

A helicopter model has been identified and validated for flight conditions defined by altitudes, speeds, loadings and centre of gravity positions. To identify the helicopter models, 2-3-1-1 multistep control inputs were performed by the pilot to excite all helicopter modes. Then, each estimated signal has to remain in tolerance margins defined by the Federal Aviation Administration. Three methods were used to observe the system outputs from its states: a fuzzy logic method, a linear method optimised with a neural network algorithm and a classical method. Because of random effects when gathering data, classical method did not give good enough results. The fuzzy logic method was not robust enough so that output plots showed peaks that could be felt by the pilot. Then, because the model could be implemented in a simulator for the pilot training, the pilot feedback is very useful in order to compare the reality with the results of the mathematical model. When the outputs are obtained from the measured state variables, the linear method gave the best results.

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1. Tischler, M.B and Remple, R.K. Aircraft and Rotorcraft System Identification: Engineering Methods with Flight Tests Examples, AIAA Education Series, AIAA, ISBN-10: 1-56347-837-4, 2006, pp 1600.
2. Hamel, P.G. and Kaletka, J. Advances in rotorcraft system identification, Progress in Aerospace Science, 1997, 33, pp 259284.
3. Hagan, M.T, Demuth, H.B. and Beale, M.H. Neural Networks Design, 1996, Boston, MA, USA, PWS Publishing.
4. Chui, S.L. A cluster estimation method with extension to fuzzy model identification, Proceedings of the 3rd IEEE Conference on Fuzzy Systems, IEEE World Congress on Computational Intelligence, 26-29 June 1994, 2, pp 12401245, Orlando, FL, USA.
5. Takagi, T. and Sugeno, M. Fuzzy identification of systems and its applications to modelling and control, IEEE Transactions on Systems, Man and Cybernetics, 1985, 15, (1), pp 116132.
6. Jategaonkar, R.V. Flight vehicle system identification: a time domain methodology, Progress in Astronautics and Aeronautics Series, Published by AIAA, ISBN-10: 1-56347-836-6, 2006, 216, 1st ed.
7. Milliken, W.F. Dynamic stability and control research, Proceedings of the 3rd Anglo-American Aeronautical Conference, Brighton, UK, 1951, pp 447524.
8. Derusso, P, Roy, R.J., Close, C.M. and Desrochers, A.A. State Variables for Engineers, 2nd ed, Wiley and Sons, New York, USA, 1998.
9. Nelles, O. Nonlinear System Identification: From Classical Approaches To Neural Networks And Fuzzy Models, ISBN-3-540-67369-5, Edition Springer-Verlag Berlin Heidelberg, New York, USA, 2001.
10. Rysdyk, R. and Calise, A.J. Robust nonlinear adaptive flight control for consistent handling qualities, IEEE Transactions on Control Systems Technology, 2005, 13, (6), pp 896910.
11. Tischler, M.B. and Kaletka, J. Modelling XV-15 tilt-rotor aircraft dynamics by frequency and time-domain identification techniques, AGARD: Rotorcraft Design for Operations, p 20, 1987.
12. Bohlin, T. Practical grey-box process identification: theory and applications, USA, London: Springer-Verlag London, 2006, (http://dx.doi.org/10.1007/1-84628-403-1).
13. Nadeau Beaulieu, M. and Botez, R.M. Simulation and prediction of the helicopter main rotor, tail rotor and engine parameters by using the subspace system identification method, J Aerospace Eng, 2008, 222 (G6), pp 817834.
14. Nadeau Beaulieu, M., Botez, R.M. and Hiliuta, A. Ground dynamics model validation by use of landing flight test, AIAA J Aircraft, 2007, 44, (6), pp 20632068.
15. Prouty, R.W. Helicopter Performance, Stability, and Control, Malabar, 2002, Flor., Krieger, R. E..
16. Elshafei, M., Akhtar, S. and Ahmed, M.S. Parametric models for helicopter identification using ANN, IEEE Transactions on Aerospace and Electronic Systems, 36, (4), pp 12421252.
17. Samal, M.K., Anavatti, S. and Garratt, M. Neural network based system identification for autonomous flight of an Eagle Helicopter, Proceedings of the 17th World Congress of the International Federation of Automatic Control, 6-11 July 2008, Seoul, Korea.
18. Montazer, Gh.A., Sabzevari, R. and Khatir, H.Gh. Improvement of learning algorithms for RBF neural networks in a helicopter sound identification system, J Neurocomputing, 2007, 71, (1-3).
19. Cabell, R.H., Fuller, C.R. and O’Brien, W.F. A neural network for the identification of measured helicopter noise, J American Helicopter Society, 1993, 38, (3).

New helicopter model identification method based on flight test data

  • S. De Jesus Mota and R. M. Botez (a1)

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