Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-25T10:16:14.407Z Has data issue: false hasContentIssue false
  • English
  • Français

Longitudinal and lateral aerodynamic characterisation of reflex wing Unmanned Aerial Vehicle from flight tests using Maximum Likelihood, Least Square and Neural Gauss Newton methods

Published online by Cambridge University Press:  10 September 2019

S. Saderla Open the ORCID record for S. Saderla [Opens in a new window] ,
R. Dhayalan ,
K. Singh ,
N. Kumar  and
A. K. Ghosh
S. Saderla*
Affiliation:
Department of Aerospace EngineeringIIT KanpurKanpurIndia
R. Dhayalan
Affiliation:
Department of Aerospace EngineeringIndian Institute of Space Science and TechnologyTrivandrumIndia
K. Singh
Affiliation:
Department of Aerospace EngineeringIIT KanpurKanpurIndia
N. Kumar
Affiliation:
Department of Aerospace EngineeringIIT KanpurKanpurIndia
A. K. Ghosh
Affiliation:
Department of Aerospace EngineeringIIT KanpurKanpurIndia
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, longitudinal and lateral-directional aerodynamic characterisation of the Cropped Delta Reflex Wing (CDRW) configuration–based unmanned aerial vehicle is carried out by means of full-scale static wind-tunnel tests followed by full-scale flight testing. A predecided set of longitudinal and lateral/directional manoeuvres is performed to acquire the respective flight data, using a dedicated onboard flight data acquisition system. The compatibility of the acquired dynamics is quantified, in terms of scale factors and biases of the measured variables, using Kinematic consistency check. Maximum likelihood (ML), least squares and newly emerging neural Gauss–Newton (NGN) methods were implemented for a wing-alone delta configuration, mainly to capture the dynamic derivatives for both longitudinal and lateral directional cases. Estimated damping and weak dynamic derivatives, which are in general challenging to capture for a wing alone configuration, are consistent using ML and NGN methods. Validation of the estimated parameters with aerodynamic model is performed by proof-of-match exercise and are presented therein.

Keywords

Reflex wingDelta configuration UAVParameter estimationMLLSNGN methods
Type
Research Article
Information
The Aeronautical Journal , Volume 123 , Issue 1269 , November 2019 , pp. 1807 - 1839
Copyright
© Royal Aeronautical Society 2019 

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

REFERENCES

Keane, A.J., Scanlan, J. and Bressloff, N.W. Conceptual design of UAV airframes using a generic geometry service, in Infotech@Aerospace, 2005, pp. 1–10.Google Scholar
Murphy, P.C. A methodology for airplane parameter estimation and confidence interval determination in nonlinear estimation problems, Ph.D. Thesisthesis, George Washington University, 1985.Google Scholar
Maine, R.E. and Iliff, K.W. Application of parameter estimation to aircraft stability and control: The output-error approach, 1987, NASA-RP-1168, H-1299, NAS 1.61:1168, NASA Technical Report Server.Google Scholar
Suk, J., Lee, Y., Kim, S., Koo, H. and Kim, J. System identification and stability evaluation of an unmanned aerial vehicle from automated flight tests, KSME Int J 2003, 17, (5), pp. 654–667.CrossRefGoogle Scholar
Chowdhary, G. and Jategaonkar, R. Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter, Aerosp Sci Technol, 2010, 14, (2), pp. 106–117.CrossRefGoogle Scholar
Condomines, J., Bronz, M. and Hattenberger, G. Experimental wind field estimation and aircraft identification, in IMAV 2015: International Micro Air Vehicles Conference and Flight Competition, 2015, pp. 1–11.Google Scholar
Meng, L., Li, L. and Veres, S.M. Aerodynamic parameter estimation of an unmanned aerial vehicle based on extended Kalman filter and its higher order approach, in 2nd International Conference on Advanced Computer Control (ICACC), 2010, vol. 5, pp. 526–531.CrossRefGoogle Scholar
Dorobantu, A., Murch, A., Mettler, B. and Balas, G. System identification for small, low-cost, fixed-wing unmanned aircraft, J Aircr, 2013, 50, (4), pp. 1117–1130.CrossRefGoogle Scholar
Padayachee, K. Aerodynamic parameter identification for an unmanned aerial vehicle, Master of Science in Engineering, Johannesburg, 2016.Google Scholar
Chase, A. and McDonald, R.A. Flight testing small UAVs for aerodynamic parameter estimation, in AIAA Atmospheric Flight Mechanics Conference, AIAA SciTech Forum, 2014, pp. 1–18.CrossRefGoogle Scholar
Hoffer, N.V., Coopmans, C., Fullmer, R.R. and Chen, Y. Small low-cost unmanned aerial vehicle System identification by Error Filtering Online Learning (EFOL) enhanced least squares method, in International Conference on Unmanned Aircraft Systems, ICUAS, 2015, pp. 1355–1363.CrossRefGoogle Scholar
Jameson, P. and Cooke, A.K. Reduced order system identification for UAVs, Aeronaut J, 2015, 119, (1218), pp. 961–980.CrossRefGoogle Scholar
Tieying, J., Jiei, L. and Kewei, H. Longitudinal parameter identification of a small unmanned aerial vehicle based on modified particle swarm optimization, Chinese J Aeronaut, 2015, 28, (3), pp. 865–873.Google Scholar
Hemakumara, P. and Sukkarieh, S. UAV parameter estimation with multi-output local and global Gaussian Process approximations, in Proceedings - IEEE International Conference on Robotics and Automation, 2013, pp. 5402–5408.CrossRefGoogle Scholar
Ben Mosbah, A., Botez, R.M. and Dao, T.-M. New methodology combining neural network and extended great deluge algorithms for the ATR-42 wing aerodynamics analysis, Aeronaut J, 2016, 120, (1229), pp. 1049–1080.CrossRefGoogle Scholar
Abdallah Ben, M., Ruxandra Mihaela, B., Thien My, D., Mohamed Sadok, G. and Mahdi, Z. A neural network controller new methodology for the ATR-42 morphing wing actuation, Incas Bull, 2016, 8, (2), pp. 59–75.CrossRefGoogle Scholar
Boëly, N., Botez, R.M. and Kouba, G. Identification of a nonlinear F/A-18 model by use of fuzzy logic and neural network methods, Proc Inst Mech Eng Part G J Aerosp Eng, 2011, 225, pp. 559–574.CrossRefGoogle Scholar
De Jesus Mota, S. and Botez, R.M. New helicopter model identification method based on a neural network optimization algorithm and on flight test data, Aeronaut J, 2011, 115, (1167), pp. 295–314.CrossRefGoogle Scholar
Tischler, M.B. and Remple, R.K. Aircraft and Rotorcraft System Identification - Engineering Methods with Flight Test Examples, Second Edition, AIAA Education Series, AIAA, 2012, Reston, VA.Google Scholar
Peyada, N.K. and Ghosh, A.K. Parameter estimation from real flight data using neural network based method, in INCPAA- 2008, Mathematical Problems in Engineering, Aerospace and Sciences, 2008.Google Scholar
Peyada, N.K. and Ghosh, A. Aircraft parameter estimation using neural network based algorithm, in AIAA Atmospheric Flight Mechanics Conference, 2009, pp. 1–13.CrossRefGoogle Scholar
Jategaonkar, R.V. Flight Vehicle System Identification - A Time Domain Methodology, AIAA Progress in Aeronautics and Astronautics AIAA, 2006, USA.CrossRefGoogle Scholar
Kumar, R. and Ghosh, A.K. Nonlinear aerodynamic modeling of Hansa-3 aircraft using neural Gauss-Newton method, J Aerosp Sci Technol AeSI, India, 2011, 63, (3), pp. 194–204.Google Scholar
Mehra, R.K., Stepner, D.E. and Tyler, J.S. Maximum likelihood identification of aircraft stability and control derivatives, J Aircr, 1974, 11, (2), pp. 81–89.CrossRefGoogle Scholar
Kumar, R. Parameter estimation using flight data of air vehicles at low and moderately high angles of attack using conventional and neural based methods, PhD thesis, Indian Institute of Technology Kanpur, 2011.Google Scholar
Dhayalan, R. Parameter estimation of flight vehicles using conventional and neural based methods, PhD thesis, Indian Institute of Technology Kanpur, 2015.Google Scholar
Kumar, R., Ganguli, R., Omkar, S.N. and Kumar, M.V. “Rotorcraft parameter identification from real time flight data,” J Aircr, 2008, 45, (1), pp. 333–341.CrossRefGoogle Scholar
Napolitano, M.R. Aircraft Dynamics: From Modeling to Simulation, John Wiley & Sons, 2012, The Atrium, Southern Gate, Chichester, West Sussex, UK.Google Scholar
Saderla, S. Parameter estimation using flight data of unmanned flight vehicles at low and moderately high angles of attack using conventional methods, PhD thesis, Indian Institute of Technoloy Kanpur, 2015.Google Scholar
Saderla, S., Rajaram, D. and Ghosh, A. Parameter estimation of unmanned flight vehicle using wind tunnel testing and real flight data, J Aerosp Eng, 2016, 30, (1), pp. 1–10.Google Scholar