Article Sidebar

Submitted
August 6, 2023
Published
August 7, 2023
Download
PDF
Statistic
Read Counter : 66 Download : 43

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Main Article Content

Abstract

A new parameter estimation method based upon Feed Forward Neural Network is proposed. The proposed method utilizes the universal mapping capability of Feed Forward Neural Network to develop flight dynamic model of aircraft. Gauss-Newton method is used to obtain values of aerodynamic parameters by minimizing a chosen error cost function. The method has then been validated using flight data pertaining to longitudinal dynamics of aircraft. Proof of match approach has been followed to verify the estimated model by the proposed method. The results obtained using the proposed method have also been compared with those obtained using wind tunnel tests, and Filter Error method. Unlike, most of the conventional methods, the proposed method does not require a priori description of the model. It also bypasses the requirement of solving the equations of motion. This feature may have special significance in handling flight data of an unstable aircraft.

Keywords

Parameter estimation, Longitudinal aerodynamic, Neural Network, Gauss-Newton method

Article Details

How to Cite
Peyada, N., & Ghosh, A. (2023). Longitudinal Aerodynamic Parameter Estimation Using Neural Network and Gauss-Newton Method. Journal of Aerospace Sciences and Technologies, 61(2), 295–304. https://doi.org/10.61653/joast.v61i2.2009.524
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Iliff, K. W., "Parameter Estimation for Flight Vehicle", Journal of Guidance, Control and Dynamics, Vol. 12, No. 5, 1989, pp. 609-622.
  2. Maine, K. E. and Iliff, K. W., "Application of Parameter Estimation to Aircraft Stability and Control: The Output-Error Approach", NASA RP 1168, January 1986.
  3. Hamel, P. G. and Jatejaonkar, R. V., "The Evolution of Flight Vehicle System Identification", AGARD, DLR Germany, 8-10, May 1995.
  4. Jategaonkar, R. V., "Flight Vehicle System Identification - A Time Domain Methodology", AIAA Progress in Aeronautics and Astronautics, Vol. 216, AIAA, Reston, VA, August 2006.
  5. Li, Ping., Postlethwaite, Ian. and Turner, Matthew., "Parameter Estimation Techniques for Helicopter Dynamic Modeling", Proceedings of the American Control Conference, July, 11-13, 2007.
  6. Hornik, K., Stinchcombe, M. and White, H., "Multi- Layer Feed Forward Networks are Universal Approximators", Neural Networks, Vol. 2, No. 5, 1989, pp. 359 366.
  7. Chen, S., Billings, S. A. and Grant, P. M., "Nonlinear System Identification using Neural Networks", International Journal of Control, 1990, Vol. 51, No. 6, pp. 1191-1214.
  8. Hess, R. A., "On the Use of Back Propagation with Feed Forward Neural Networks for Aerodynamic Estimation Problem", AIAA Paper 93-3638, August 1993.
  9. Youseff, H. M., "Estimation of Aerodynamic Coefficients using Neural Networks", AIAA Paper 93- 3639, August 1993.
  10. Bassapa. and Jategaonkar, R. V., "Aspects of Feed Forward Neural Network Modeling and its Application to Lateral-Directional Flight Data", DLR-IB 111-95/30, Braunschweig, Germany, September 1995.
  11. Linse, D. J. and Stengel, R. F., "Identification of Aerodynamic Coefficients using Computational Neural Networks", Journal of Guidance, Control and Dynamics, Vol. 16, No. 6, 1993, pp. 1018 1025.
  12. Raol, J. R. and Jategaonkar, R. V., "Aircraft Parameter Estimation using Recurrent Neural Networks - A Critical Appraisal", AIAA Atmospheric Flight Mechanics Conference, Baltimore, Maryland, USA, August 7-9, 1995, Paper No. AIAA 95-3504.
  13. Raisinghani, S. C., Ghosh, A. K. and Kalra, P. K., "Two New Techniques for Aircraft Parameter Estimation using Neural Networks", Aeronautical Journal, Vol. 102, No. 1011, January 1998, pp. 25-29.
  14. Ghosh, A. K., Raisinghani, S. C. and Khubchandani, S., "Estimation Of Aircraft Lateral-Directional Parameters Using Neural Networks", Journal of Aircraft, Vol. 35, No. 6, Nov-Dec. 1998, pp. 876-881.
  15. Singh, S. and Ghosh, A. K., "Parameter Estimation from Flight Data of a Missile using Maximum Likelihood and Neural Network Method", AIAA Atmospheric Flight Mechanics Conference and Exhibit, Keystone, Colorado, USA, August 21-24, 2006, Paper No. AIAA 2006-6284.
  16. Ghosh, A. K.and Raisinghani, S. C., "Parameter Estimation from Flight Data of an Unstable Aircraft using Neural Networks", Journal of Aircraft, Vol. 39, No. 5, 2002, pp. 892-894.
  17. Gauche, E., Coelho, J. and Teive, R. C. G., "A Mixed Back-Propagation/Marquardt-Levenberg Algorithm for Optimizing the Distribution Electrical Systems Operation", IEEE, 1999, pp. 753-758.
  18. Hagan, M. T. and Menhaj, M. B., "Training Feed Forward Networks with the Marquardt Algorithm", IEEE Transaction on Neural Networks, 1994, 5 (6), pp. 989-993.
  19. Lee, C. C., Chung, P. C., Tsai, J. R. and Chang, C. I., "Robust Radial Basis Function Neural Networks", IEEE Transaction on System, Man and Cybernetics- Part B, December 1999, 29 (6), pp. 674-685.
  20. Saha, A., Wu, C. L. and Tang, D. S., "Approximation, Dimension Reduction and Nonconvex Optimization using Linear Superposition of Gaussians", IEEE Transaction on Computers, 1993, 42, pp.1222-1233.
  21. Singh, S. and Ghosh, A. K., "Estimation of Lateraldirectional Parameters Using Neural Networks Based Modified Delta Method", Aeronautical Journal, Vol. 111, No. 1124, September 2007, pp. 659- 667.
  22. Rangaranjan, R. and Viswanathan, S., "Wind Tunnel Test Results on A 1/5 Scale HANSA Model", NAL TR-01, Vol.1, December 1997.

Most read articles by the same author(s)