Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-28T22:08:03.412Z Has data issue: false hasContentIssue false
  • English
  • Français

Application of aerodynamic model structure determination to UAV data

Published online by Cambridge University Press:  27 January 2016

S. Carnduff  and
A. Cooke
A. Cooke
Affiliation:
Department of Aerospace Sciences, Cranfield University, Cranfield, UK
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper concerns aircraft system identification and, in particular, the process of aerodynamic model structure determination. Its application to experimental data from unmanned aerial vehicles (UAVs) is also described. The procedure can be particularly useful for determining an aerodynamic model for aircraft with unconventional airframe configurations, which some unmanned aircraft tend to have. Two model structure determination techniques are outlined. The first is the well-established stepwise regression method, while the second is an adaptation of an existing frequency response approach which instead utilises maximum likelihood estimation. Example applications of the methods are presented for two data sources. The first is a set of UAV flight test data and the second is data recorded from dynamic wind tunnel tests on a UAV configuration. For both examples, the model structures determined using stepwise regression and maximum likelihood analysis matched one another, suggesting that the maximum likelihood approach and the chosen thresholds for its statistical metrics were reliable for the data being analysed.

Type
Research Article
Information
The Aeronautical Journal , Volume 115 , Issue 1170 , August 2011 , pp. 481 - 492
Copyright
Copyright © Royal Aeronautical Society 2011 

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. Klein, V. and Morelli, E.A. Aircraft system identification: Theory and practice, 2006, American Institute of Aeronautics and Astronautics, Reston, VA, USA.Google Scholar
2. Jategaonkar, R.V. Flight vehicle system identification: A time domain methodology, 2006, American Institute of Aeronautics and Astronautics, Reston, VA, USA.Google Scholar
3. Tischler, M.B. and Remple, R.K. Aircraft and rotorcraft system identification: Engineering methods with flight test examples, 2006, American Institute of Aeronautics and Astronautics, Reston, VA, USA.Google Scholar
4. Klein, V. On the adequate model for aircraft parameter estimation, 1975, College of Aeronautics Aero Report 28, Cranfield Institute of Technology, UK.Google Scholar
5. Taylor, L.W. Application of new criterion for modeling systems, May 1975, AGARD CP-172, 4.1-4.9.Google Scholar
6. Hall, W.E., Gupta, N.K. and Tyler, J.S. Model structure determination and parameter identification for nonlinear aerodynamic flight regimes, May 1975, AGARD CP-172, 21.1-21.21.Google Scholar
7. Gupta, N.K., Hall, W.E. and Trankle, T.L. Advanced methods of model structure determination from test data, J Guidance, Control and Dynamics, 1978, 1, (3), pp 197–204.Google Scholar
8. Klein, V., Batterson, J.G. and Murphy, P.C. Determination of airplane model structure from flight data using modified stepwise regression, October 1981, NASA TP-1916.Google Scholar
9. Klein, V. and Batterson, J.G. Determination of airplane model structure from flight data using splines and stepwise regression, March 1983, NASA TP-2126.Google Scholar
10. Milne, G.W. Identification of a Dynamic Model of a Helicopter from Flight Tests, 1986, PhD thesis, Stanford University, Department of Aeronautics and Astronautics, CA, USA.Google Scholar
11. Carnduff, S. System Identification of Unmanned Aerial Vehicles, 2008, PhD thesis, School of Engineering, Cranfield University, UK.Google Scholar
12. Flux, P. Some stability and control aspects of UCAV configurations, 2003, CEAS Aerospace Aerodynamics Research Conference, June 2003, London, UK.Google Scholar
13. Montgomery, D.C., Peck, E.A. and Vining, G.G. Introduction to Linear Regression Analysis, 2001, Wiley, New York, USA.Google Scholar
14. Kreyszig, E. Advanced Engineering Mathematics, 1999, Eighth edition, John Wiley, New York, USA.Google Scholar
15. Tischler, M.B. and Kaletka, J. Modeling XV-15 tilt-rotor aircraft dynamics by frequency and time-domain identification techniques, December 1986, NASA-TM-89404.Google Scholar
16. Iliff, K.W. and Taylor, L.W. Determination of stability derivatives from flight data using a Newton-Raphson minimization technique, 1972, NASA TN D-6579.Google Scholar
17. Maine, R.E. and Iliff, K.W. The theory and practice of estimating the accuracy of dynamic flight-determined coefficients, 1981, NASA Reference Publication 1077.Google Scholar
18. Morelli, E.A. and Klein, V. Accuracy of aerodynamic model parameters estimated from flight test data, J Guidance, Control and Dynamics, 1997, 20, (1), pp 74–80.Google Scholar
19. ESDU Aerodynamics Series, Engineering Sciences Data Unit, ESDU International, London, UK.Google Scholar
20. USAF stability and control DATCOM, Flight Control Division, US Air Force Flight Dynamics Laboratory, Wright-Patterson Air Force Base, Fairborn, OH, USA.Google Scholar
21. Carnduff, S.D., Erbsloeh, S.D., Cooke, A.K. and Cook, M.V. Characterizing stability and control of subscale aircraft from windtunnel dynamic motion, J Aircr, 2009, 46, (1), pp 137–147.Google Scholar
22. McDougall, N. BAE Systems/EPSRC Integrated research Programme in aeronautical engineering, Aeronaut J, 2006, 110, (1104), pp 121–124.Google Scholar
23. Carnduff, S.D. and Cooke, A.K. Formulation and system identification of the equations of motion for a dynamic wind tunnel facility, March 2008, College of Aeronautic Report 0801, Cranfield University, UK.Google Scholar
24. Cook, M.V. Flight Dynamics Principles, 1997, Edward Arnold, London, UK.Google Scholar
25. Marchand, M. and Fu, K.H. Frequency domain parameter estimation of aeronautical systems without and with time delays, 1985, proceedings of 7th IFAC Symposium on Identification and System Parameter Estimation, July 1985, pp 669–674, Pergamon, Oxford, UK.Google Scholar