Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-28T22:42:51.277Z Has data issue: false hasContentIssue false

Reduced order system identification for UAVs

Published online by Cambridge University Press:  27 January 2016

P-D. Jameson
Affiliation:
pierre_jameson@msn.com
A. K. Cooke
Affiliation:
a.cooke@cranfield.ac.uk, Dynamics, Simulation and Control Group Centre for Aeronautics, Cranfield University, Bedfordshire, UK

Abstract

Reduced order models representing the dynamic behaviour of symmetric aircraft are well known and can be easily derived from the standard equations of motion. In flight testing, accurate measurements of the dependent variables which describe the linearised reduced order models for a particular flight condition are vital for successful system identification. However, not all the desired measurements such as the rate of change in vertical velocity () can be accurately measured in practice. In order to determine such variables two possible solutions exist: reconstruction or differentiation. This paper addresses the effect of both methods on the reliability of the parameter estimates. The methods are used in the estimation of the aerodynamic derivatives for the Aerosonde UAV from a recreated flight test scenario in Simulink. Subsequently, the methods are then applied and compared using real data obtained from flight tests of the Cranfield University Jetstream 31 (G-NFLA) research aircraft.

Type
Research Article
Copyright
Copyright © Royal Aeronautical Society 2015

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.Iliff, K.W.. Parameter estimation for flight vehicles, J Guidance, Control, and Dynamics, September-October 1989, 12, pp 609622.CrossRefGoogle Scholar
2.Hamel, P.G. and Jategaonkar, R.. Evolution of flight vehicle system identification, J Aircr, January-February 1996, 33, (1), pp 928.Google Scholar
3.Carnduff, S.D.. System Identification of Unmanned Aerial Vehicles, 2008, PhD thesis, Cranfield University, UK.Google Scholar
4.Photo Aerosonde, Atlantic 1998, Wallpaper free for common use, taken from Aerosonde Pty Ltd.www.aerosonde.com, 11/04/2005.Google Scholar
5.Zadeh, L.A.. From circuit theory to system theory, Proceedings of the IRE, May 1962, 50, pp 856865.CrossRefGoogle Scholar
6.Klein, V.. Estimation of aircraft aerodynamic parameters from flight data, Prog in Aerospace Sci, 1989, 26, (1), pp 177.Google Scholar
7.Klein, V.. A review of system identification methods applied to aircraft, 1983, Technical Report, Joint Institute for Acoustics and Flight Sciences Report, N83 33901, The George Washington University.Google Scholar
8.Cook, M.V., Flight Dynamic Principles: A Linear Systems Approach to Aircraft Stability and Control, 2007, Elsevier, Amsterdam, The Netherlands.Google Scholar
9.Klein, V. and morelli, E.A., Aircraft System Identification: Theory and Practice, 2006, AIAA, Reston, VA, USA.CrossRefGoogle Scholar
10.Chowdhary, G., DeBusk, W.M. and Johnson, E.N., Real-time system identification of a small multi-engine aircraft with structural damage, 2010, AIAA-2010-3472, AIAA Infotec@Aerospace, 20-22 April 2010, Atlanta, GA, USA.Google Scholar
11.Jategaonkar, R., Flight Vehicle System Identification: A Time Domain Methodology, 2006, AIAA, Reston, VA, USA.CrossRefGoogle Scholar
12.Morelli, E.A.. Practical aspects of the equation-error method for aircraft parameter estimation, 2006, AIAA-2006-6144, AIAA Atmospheric Flight Mechanics Conference and Exhibition, 21-24 August 2006, Keystone, CO, USA.Google Scholar
13.Lanczos, C., Applied Analysis, 1957, Sir Isaac Pitman & Sons, London, UK.Google Scholar
14.Etkin, B., Dynamics of Atmospheric Flight, 1972, John Wiley & Sons.Google Scholar
15.Mulder, J.A.. Design and Evaluation of Dynamic Flight Test Manoeuvres, 1986, PhD thesis, Delft University of Technology, The Netherlands.Google Scholar
16.Whidborne, J.F., A note on the Aerosonde model, 1st edition, July 2007, Internal report, Cranfield University, UK.Google Scholar
17.Foster, G.W.. The Identification of Aircraft Stability and Control Parameters in Turbulence, 1982, PhD thesis, Cranfield Institute of Technology, UK.Google Scholar
18.Morelli, E.A. and Smith, M.S.. Real-time dynamic modeling: data information requirements and flight test results, J Aircr, November-December 2009, 46, (6), pp 18941905.Google Scholar
19.Jameson, P-D.. Post-Manoeuvre and Online Parameter Estimation for Manned and Unmanned Aircraft, 2013, PhD thesis, Cranfield University, UK.Google Scholar
20.Mullen, G.J.. Aircraft parameter identification using MATLAB, 2000, Technical Report College of Aeronautics, Report 0011, Cranfield University, UK.Google Scholar