Hostname: page-component-7bb8b95d7b-w7rtg Total loading time: 0 Render date: 2024-09-19T11:06:32.579Z Has data issue: false hasContentIssue false
  • English
  • Français

Estimation of lateral-directional aerodynamic derivatives from flight data using conventional and neural based methods

Published online by Cambridge University Press:  27 January 2016

R. Kumar  and
A. K. Ghosh
R. Kumar*
Affiliation:
Department of Aerospace Engineering, PEC University of Technology, Sector-12, Chandigarh, India
A. K. Ghosh*
Affiliation:
Department of Aerospace Engineering, Indian Institute of Technology, Kanpur, India
*
rakpec@gmail.com
akg@iitk.ac.in
Get access Rights & Permissions [Opens in a new window]

Abstract

The paper presents the estimation of lateral-directional aerodynamic derivatives (parameters) using conventional and neural based methods from real flight data of Hansa-3 aircraft. The conventional methods such as least squares (LS) and maximum likelihood (ML) require an a priori postulation of mathematical model to estimate the parameters. Whereas the neural-based method such as Neural-Gauss-Newton (NGN) is an algorithm that utilises feed forward neural network and Gauss-Newton optimisation to estimate the parameters and does not require any a priori postulation of mathematical model or solution of equations of motion. In the paper, the LS, ML and NGN methods are applied to lateral-directional flight data in order to estimate parameters. The results obtained in terms of lateral-directional aerodynamic derivatives are reasonably accurate to establish LS, ML and NGN as parameter estimation methods along with NGN method having an additional advantage of non-requirement of a priori mathematical model. The paper also highlights the effect of different types of control inputs on parameter estimation. For this, three types of control inputs were used to generate real flight data. The ailerons and rudder were deflected in the first, the ailerons were deflected while keeping rudder at trim condition in the second and the rudder was deflected while keeping ailerons at trim condition in the third type of control input to generate the real flight data. The paper presents the effect of three different types of control inputs in terms of aerodynamic parameters estimated through conventional and neural based methods using flight data generated through these inputs.

Type
Research Article
Information
The Aeronautical Journal , Volume 118 , Issue 1210 , December 2014 , pp. 1453 - 1479
Copyright
Copyright © Royal Aeronautical Society 2014 

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. Greenberg, H. A survey of methods for determining stability parameters of an airplane from dynamic fight measurements, April 1951, NACA TN-2340.Google Scholar
2. Shinbrot, M. A least square curve ftting method with applications to the calculation of stability coeff-cients from transient response data, April 1951, NACA TN 2341.Google Scholar
3. Mehra, R.K. Maximum likelihood identifcation of aircraft parameters, 1970, Proceedings of the 11th Joint Automatic Control Conference, Atlanta, GA.Google Scholar
4. Roskam, J. Methods for estimating stability and control derivatives for conventional subsonic airplanes, 1973, Roskam Aviation and Engineering Corporation.Google Scholar
5. Maine, R.E. and Iliff, K.W. Identifcation of dynamic systems — Application to aircraft — Part 1: Output error approach, AGARD-AG-300, December 1986, 3, (1).Google Scholar
6. Youssef, H.M. and Jaung, J.C. Estimation of aerodynamic coeffcients using neural networks, August 1993, AIAA Paper 93-3639.Google Scholar
7. Basappa, K. and Jategaonkar, R.V. Aspects of feed forward neural network modeling and its application to lateral-directional fight-data, September 1995, DLR IB 111-95/30.Google Scholar
8. Hamel, P.G. and Jategaonkar, R.V. The evolution of fight vehicle system identifcation, 8-10 May 1995, AGARD, DLR Germany.Google Scholar
9. 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 25–29.Google Scholar
10. Ghosh, A.K. Aircraft Parameter Estimation from Flight Data using Feed Forward Neural Networks, April 1998, PhD thesis, Aerospace Engineering Dept, IIT Kanpur.Google Scholar
11. Klein, V. and Morelli, E.A. Aircraft System Identifcation — Theory and Practice, 2006, AIAA Education Series, Reston, VA, USA.Google Scholar
12. Jategaonkar, R.V. Flight vehicle system identifcation — A time domain methodology, AIAA Progress in Aeronautics and Astronautics, August 2006, 216, AIAA, Reston, VA, USA.Google Scholar
13. Singh, S. Estimation of Aircraft Parameters from Flight Data using Neural Network Based Method, April 2007, PhD thesis, Aerospace Engineering Dept, IIT Kanpur.Google Scholar
14. Peyada, N.K., and Ghosh, A.K. Aircraft parameter estimation using new filtering technique based on neural network and Gauss-Newton method, Aeronaut J, April 2009, 113, (1142).Google Scholar
15. Kumar, R. and Ghosh, A.K. Nonlinear longitudinal aerodynamic modeling using neural Gauss-Newton Method, J Aircr, September-October 2011, 48, (5), pp 1809–1812.Google Scholar
16. Kumar, R. Parameter Estimation Using Flight Data of Air Vehicles at Low and Moderately High Angles of Attack using Conventional and Neural Based Methods, November, 2011, PhD thesis, Aerospace Engineering Department, IIT Kanpur, India.Google Scholar