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Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

Published online by Cambridge University Press:  15 May 2018

Subrahmanyam Saderla ,
Dhayalan Rajaram  and
A. K. Ghosh
Subrahmanyam Saderla*
Affiliation:
Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, India
Dhayalan Rajaram
Affiliation:
Department of Aerospace Engineering, Indian Institute of Space Science and Technology, Trivandrum, India
A. K. Ghosh
Affiliation:
Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, India
*
subrahmanyam.saderla@gmail.com
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Abstract

The current research paper describes the lateral-directional parameter estimation from flight data of a miniature Unmanned Aerial Vehicle (UAV) using Maximum Likelihood (ML), and Neural-Gauss-Newton (NGN) methods. An unmanned configuration with a cropped delta planform and thin rectangular cross-section has been designed, fabricated and instrumented. Exhaustive full-scale wind-tunnel tests were performed on the UAV to extract the form of aerodynamic model that has to be postulated a priori for parameter estimation. Rigorous flight tests have been performed to acquire the flight data for several prescribed manoeuvres. Four sets of compatible flight data have been used to carry out parameter estimation using classical ML and neural-network-based NGN methods. It is observed that the estimated parameters are consistent and the lower values of the Cramer-Rao bound for the corresponding estimates have shown significant confidence in the obtained parameters. Furthermore, to validate the aerodynamic model used and to enhance the confidence in the estimated parameters, a proof of match exercise has been carried out.

Keywords

UAV, aerodynamic characterisationflight datamaximum likelihoodleast squareNeural-Gauss-Newton
Type
Research Article
Information
The Aeronautical Journal , Volume 122 , Issue 1252 , June 2018 , pp. 889 - 912
Copyright
Copyright © Royal Aeronautical Society 2018 

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References

REFERENCES

1. Austin, R. Unmanned Aircraft Systems, 2010, John Wiley & Sons, West Sussex, UK.CrossRefGoogle Scholar
2. Jategaonkar, R. V. Flight vehicle system identification - a time domain methodology, AIAA Progress in Aeronautics and Astronautics, 2006, AIAA, Reston, VA, US.Google Scholar
3. Milliken, W. F. Progress in dynamic stability and control research, J Aeronautical Sciences, 1947, 14, (9), pp 493-519.CrossRefGoogle Scholar
4. Greenberg, H. A survey of methods for determining stability parameters of an airplane from dynamic flight measurements, Washington, 1951, NACA TN-2340, Washington, US.Google Scholar
5. Shinbrot, M. A least square curve fitting method with applications to the calculation of stability coefficients from transient response data, 1951, NACA TN 2341, Moffett Field, CA, US.Google Scholar
6. Goman, M. and Khrabrov, A. State-space representation of aerodynamic characteristics of an aircraft at high angles of attack, J Aircraft, 1990, 31, (5), pp 1109-1115.CrossRefGoogle Scholar
7. Leishman, J. G. and Nguyen, K. Q. State-space representation of unsteady airfoil behavior, AIAA J, 1990, 28, (5), pp 836-844.CrossRefGoogle Scholar
8. Nelson, R. C. and Pelletier, A. The unsteady aerodynamics of slender wings and aircraft undergoing large amplitude maneuvers, Progress in Aerospace Sciences, 2003, 39, (2–3), pp 185-248, Available at: https://doi.org/10.1016/S0376-0421(02)00088-X.CrossRefGoogle Scholar
9. Fischenberg, D. and Jategaonkar, R. V. Identification of aircraft stall behavior from flight test data, 20th Atmospheric and Flight Mechanics Conference, 1995, Baltimore, Maryland, US, pp 138-146.Google Scholar
10. Peyada, N. K. and Ghosh, A. K. Parameter estimation from real flight data using neural network based method, INCPAA- 2008, Mathematical Problems in Engineering, Aerospace and Sciences, 2008, Genoa, Italy.Google Scholar
11. 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, 2011, Indian Institute of Technology Kanpur, India.Google Scholar
12. 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, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2011, 225, pp 559-574. doi: 10.1177/2041302510392871.CrossRefGoogle Scholar
13. 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, Aeronautical J, 2011, 115, (1167), pp 295-314, doi: 10.1017/S0001924000005789.CrossRefGoogle Scholar
14. 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, Aeronautical J, 2016, 120, (1229), pp 1049-1080, doi: 10.1017/aer.2016.46.CrossRefGoogle Scholar
15. 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, http://bulletin.incas.ro/volume_8_issue_22016.html.CrossRefGoogle Scholar
16. Morelli, E. System identification programs for aircraft (SIDPAC), AIAA Atmospheric Flight Mechanics Conference and Exhibit, 2002, Monterey, California, US. pp 1-19. doi: 10.2514/6.2002-4704.Google Scholar
17. Morelli, E. A. Real-time aerodynamic parameter estimation without air flow angle measurements, J Aircr, 2012, 49, (4), pp 1064-1074. doi: 10.2514/1.C031568.CrossRefGoogle Scholar
18. Klein, V. and Morelli, E. A. Aircraft System Identification - Theory and Practice, 2006, AIAA Education Series, Inc., Reston, Virginia, US.CrossRefGoogle Scholar
19. Tischler, M. B. and Remple, R. K. Aircraft and Rotorcraft System Identification - Engineering Methods with Flight Test Examples, 2nd ed, 2012, AIAA Education Series, AIAA, Reston, Virginia, US. Available at: https://doi.org/10.2514/4.861352.Google Scholar
20. Ivler, C. and Tischler, M. System identification modeling for flight control design, In RAeS Rotorcraft Handling-Qualities Conference, 4-6 November 2008, University of Liverpool, UK.Google Scholar
21. Colin, T., Colbourne, J. D. and Tischler, M. B. Rapid frequency-domain modeling methods for unmanned aerial vehicle flight control applications, J Aircr, 2004, 41, (4), pp 735-743, Available at: https://doi.org/10.2514/1.4671.Google Scholar
22. Downs, J., Prentice, R., Dalzell, S., Besachio, A., Ivler, C M., Tischler, M B. and Mansur, M. H. Control system development and flight test experience with the MQ-8B fire scout vertical take-off unmanned aerial vehicle (VTUAV), American Helicopter Society International 63rd Annual Forum - Riding the Wave of New Vertical Flight Technology, 1-3 May 2007, Virginia Beach, VA, US, pp 566-592.Google Scholar
23. Balakrishnan, A. V. Stochastic system identification techniques, Proceedings of the Advanced Seminar on Stochastic Optimization and Control, 1968, Wiley, New York.Google Scholar
24. Rakesh, K. and Ghosh, A. K. Parameter estimation from real flight data of Hansa-3 aircraft using three different estimation methods, International Conference on Theoretical, Applied, Computational and Experimental Mechanics (ICTACEM-10), IIT Kharagpur, India.Google Scholar
25. Dhayalan, R. Parameter Estimation of Flight Vehicles using Conventional and Neural Based Methods, PhD thesis, 2015, Indian Institute of Technology Kanpur, India.Google Scholar
26. Napolitano, M. R. Aircraft Dynamics: From modelling to Simulation, 2012, John Wiley & Sons, West Sussex, UK.Google Scholar
27. Saderla, S. Parameter Estimation using Flight Data of Unmanned Flight Vehicles at Low and Moderately High Angles of Attack using Conventional Methods, PhD thesis, 2015, Indian Institute of Technoloy Kanpur, India.Google Scholar
28. Chandra, B., Gupta, R. and Sharma, G. National wind tunnel facility, IIT Kanpur – calibration aspects, Recent Advances in Experimental Mechanics, Proceedings National Symposium on Recent Advances in Experimental Mechanics, March, 2000, IIT Kanpur, India, pp 294-307.Google Scholar
29. Saderla, S., Rajaram, , D. and Ghosh, A. Parameter estimation of unmanned flight vehicle using wind tunnel testing and real flight data, Journal of Aerospace Engineering, 30, (1), 2016, doi: 10.1061/(ASCE)AS.1943-5525.0000679.Google Scholar
30. Saderla, S., Dhayalan, R. and Ghosh, A. K. Non-linear aerodynamic modelling of unmanned cropped delta configuration from experimental data, Aeronautical J, 2017, pp 1-21. doi: 10.1017/aer.2016.124.Google Scholar
31. Saderla, S., Dhayalan, R. and Ghosh, A. K. Parameter estimation from near stall flight data using conventional and neural-based methods, Defense Science J, DRDO - India, 2017, 67, (1), pp 3-11. doi: 10.14429/dsj.67.9995.Google Scholar
32. Saderla, S., Dhayalan, R, and Ghosh, A. K. Longitudinal parameter estimation from real flight data of unmanned cropped delta flat plate configuration, Int J Intelligent Unmanned Systems, 2016, 4, (1), pp 2-22. doi: 10.1108/IJIUS-07-2015-0008.CrossRefGoogle Scholar