Online Methods for the Identification of Aircraft Model: Review and Comparison

Document Type : Dynamics, Vibrations, and Control

Authors

1 Electrical Engineering Department, Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran

2 Electrical Engineering Department, Faculty of Technical and Engineering, Imam Khomeini International University

Abstract

In this paper, a fairly comprehensive comparative study has been done on online identification methods for the dynamic model of an aircraft system. To this aim, first, the existing algorithms in this field are introduced. Then, some of these approaches including the recursive least square, the recursive extended least square, the recursive instrumental variable, the extended matrix, the radial basis function neural network, and the multilayer perceptron neural network are utilized to identify the aircraft model. To carry out the simulations, and train the neural networks, the linearized model and online data of the Boeing 747 aircraft controlled by a sliding mode controller on an arbitrary reference trajectory are employed. Finally, the efficiency of each of the above-mentioned methods is evaluated and compared to the other approaches. According to the obtained results of this research, the radial basis function neural network method has a significantly superior performance over the other algorithms due to dynamic noise estimation, independence from the system model, rejecting the linear model of the system, and higher accuracy while maintaining the appropriate speed.

Keywords

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References

  1. Nguyen, N.T. and K. Krishnakumar, Hybrid intelligent flight control with adaptive learning parameter estimation. Journal of Aerospace Computing, Information, and Communication, Vol. 6, No.3, pp. 171-186, 2009.##
  2. DeBusk, W., E. Johnson, and G. Chowdhary. Real-time system identification of a small multi-engine aircraft. in AIAA Atmospheric Flight Mechanics Conference, 2009.##
  3. Birnbaum, Z., et al., Unmanned aerial vehicle security using recursive parameter estimation. Journal of Intelligent & Robotic Systems,
    84, No. 1-4 pp. 107-120, 2016.##
  4. Hatamleh, K., O. Ma, and R. Paz. In-flight UAV model parameter identification: A simulation study. in AIAA Atmospheric flight mechanics conference. 2009.##
  5. Lee, R. and L. Shen, System identification of cessna 182 model uav. preprinted, 2005.##
  6. Zhe, L., Z. Yue-rong, and W. Gui-dong. Online parameter identification study on a small fixed-wing UAV. in IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), 2016. IEEE.##
  7. Hoffer, N.V., et al. 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. IEEE.##
  8. Raptis, I.A., K.P. Valavanis, and W.A. Moreno, System identification and discrete nonlinear control of miniature helicopters using backstepping. Journal of Intelligent and Robotic Systems, Vol. 55, No. 2-3,
    223-243, 2009.##
  9. Toha, S. and M. Tokhi, Parametric modelling application to a twin rotor system using recursive least squares, genetic, and swarm optimization techniques. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering,
    224, No. 9, pp. 961-977. 2010##
  10. Kim, W.-J., J. LEE, and D. LEE. Application of recursive partially unknown system identification to aerodynamic coefficients estimation. in Astrodynamics Conference, 1992.##
  11. Grauer, J.A. Parameter uncertainty for aircraft aerodynamic modeling using recursive least squares. in AIAA atmospheric flight mechanics conference. 2016.##
  12. Basappa, K. and R. Jategaonkar. Evaluation of recursive methods for aircraft parameter estimation. in AIAA Atmospheric Flight Mechanics Conference and Exhibit. 2004.##
  13. Qadri, M.T., et al., Comparative analysis of LMS (Least Mean Square) and RLS (Recursive Least Square) for estimation of fighter plane's mathematical model. Journal of Information and Communication Technology, Vol. 10,
    1, pp. 35-46, 2016.##
  14. Andrzejczak, V., Methods applied to aircraft identification. 2011.##
  15. Singh, S. and A. Ghosh. Parameter estimation from flight data of a missile using maximum likelihood and neural network method. in AIAA Atmospheric Flight Mechanics Conference and Exhibit, 2006.##
  16. Saderla, S., Y. Kim, and A. Ghosh, Online system identification of mini cropped delta UAVs using flight test methods. Aerospace Science and Technology, Vol. 80,
    337-353, 2018.##
  17. Ma, L. and X. Liu, Recursive maximum likelihood method for the identification of Hammerstein ARMAX system. Applied Mathematical Modelling,. Vol. 40, No. 13-14,
    6523-6535, 2016##
  18. Hu, C. and Q. Liu. Online identification for hypersonic vehicle using recursive maximum likelihood method based on interior-point algorithm. in 25th Chinese Control and Decision Conference (CCDC), 2013. IEEE.##
  19. Söderström, T. and P. Stoica, Instrumental variable methods for system identification. Circuits, Systems and Signal Processing,
    21, No.1, pp. 1-9, 2002.##
  20. Cedervall, M. and P. Stoica, System identification from noisy measurements by using instrumental variables and subspace fitting. Circuits, Systems and Signal Processing, Vol. 15 , No. 2, pp. 275-290, 1996.##
  21. Chen, X. and H.-T. Fang, Recursive subspace method for wiener systems using instrumental variable techniques. IFAC Proceedings,
    45, No. 16, pp. 1508-1513, 2012.##
  22. Friedlander, B., The overdetermined recursive instrumental variable method. IEEE Transactions on Automatic Control, Vol. 29 No.4, pp. 353-356, 1984.##
  23. HUFFEL, S.V. and J. Vandewalle, Comparison of total least squares and instrumental variable methods for parameter estimation of transfer function models. International journal of control,Vol. 50, No.4, 1039-1056, 1989.##
  24. Ma, L. and X. Liu, A nonlinear recursive instrumental variables identification method of Hammerstein ARMAX system. Nonlinear Dynamics,Vol. 79, No.2, pp. 1601-1613, 2015.##
  25. Strobach, P., Bi-iteration recursive instrumental variable subspace tracking and adaptive filtering. IEEE transactions on signal processing,Vol. 46, No.10 pp. 2708-2725,1998.##
  26. Young, P.C., An instrumental variable method for real-time identification of a noisy process. Automatica,Vol. 6, No.2, pp. 271-287,1970.##
  27. Young, P.C., Refined instrumental variable estimation: maximum likelihood optimization of a unified Box–Jenkins model. Automatica,. Vol. 52, pp. 35-46. 2015##
  28. Chen, H. Extended recursive least squares algorithm for nonlinear stochastic systems. in Proceedings of the American Control Conference. 2004. IEEE.##
  29. Hu, Y., et al., Recursive extended least squares parameter estimation for Wiener nonlinear systems with moving average noises. Circuits, Systems, and Signal Processing,Vol. 33, No.2, pp. 655-664. 2014##

 

  1. Raisinghani, S., A. Ghosh, and P. Kalra, Two new techniques for aircraft parameter estimation using neural networks. The aeronautical journal, Vol. 102, No. 1011, pp. 25-301998.##
  2. Ghazi, G., et al. Cessna citation X stall characteristics identification from flight data using neural networks. in AIAA Atmospheric Flight Mechanics Conference. 2017.##
  3. Ghosh, A. and S. Raisinghani, Frequency-domain estimation of parameters from flight data using neural networks. Journal of Guidance, Control, and Dynamics, Vol. 24, No.3, pp. 525-530, 2001.##
  4. Kumar, R., et al., Rotorcraft parameter identification from real time flight data. Journal of Aircraft,Vol. 45, No.1, pp. 333-341, 2008.##
  5. Raol, J. and R. Jategaonkar. Aircraft parameter estimation using recurrent neural networks-A critical appraisal. in 20th Atmospheric Flight Mechanics Conference. 1995.##
  6. Wallach, R., et al. Aerodynamic coefficient prediction of transport aircraft using neural network. in 44th AIAA Aerospace Sciences Meeting and Exhibit. 2006.##
  7. Muñoz, R.S.M., C. Rossi, and A.B. Cruz, Modelling and Identification of Flight Dynamics in Mini-Helicopters Using Neural Networks, in Aerial Vehicles. 2009, IntechOpen.##
  8. Kumar, M.V., et al., Identification of helicopter dynamics using recurrent neural networks and flight data. Journal of the American Helicopter Society,Vol. 51, No.2, pp. 164-174, 2006.##
  9. Suresh, S., et al. Neural networks based identification of helicopter dynamics using flight data. in Proceedings of the 9th International Conference on Neural Information Processing. 2002. IEEE.##
  10. Chen, W. and M. Saif, Adaptive actuator fault detection, isolation and accommodation in uncertain systems. International Journal of Control,Vol. 80, No.1, pp. 45-63, 2007.##
  11. Faris, H., I. Aljarah, and S. Mirjalili, Evolving radial basis function networks using moth–flame optimizer, in Handbook of Neural Computation., Elsevier. pp. 537-550, 2017.##
  12. karari, m., System identification. 1394, Tehran: Amir kabir university. (In persian).##
Aerospace Mechanics
Volume 17, Issue 4 - Serial Number 66
February 2022
Pages 109-117

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History

  • Receive Date: 10 December 2019
  • Revise Date: 12 January 2020
  • Accept Date: 09 February 2022
  • Publish Date: 21 January 2022

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