Identification of the Dynamics of a Moving Object with the Use of Neural Networks

  • Yu. N. Zolotukhin
  • K. Yu. KotovEmail author
  • A. M. Svitova
  • E. D. Semenyuk
  • M. A. Sobolev
Modeling in Physical and Technical Research


A method for identification of the dynamics of a quadrotor-type vehicle is proposed. The method is based on the Elman recurrent neural network, which corresponds to the canonical form of a dynamic system in the space of states and does not require structural correction. The results of a numerical experiment reveal the convergence of the network learning algorithm with the use of an extended Kalman filter.


identification of the dynamics quadrotor extended Kalman filter Elman recurrent neural network 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    S. Khaikin, Neural Networks: Full Course (Williams Publishing House, Moscow, 2006) [in Russian].Google Scholar
  2. 2.
    V. S. Brusov and Yu. V. Tyumentsev, Neural Modeling of Motion of Flying Vehicles (MAI, Moscow, 2016) [in Russian].Google Scholar
  3. 3.
    S. A. Belokon’, Yu. N. Zolotukhin, and M. N. Filippov, “Method of Test Signal Design for Estimating the Aircraft Aerodynamic Parameters,” Avtometriya 53 (4), 59–65 (2017) [Optoelectron., Instrum. Data Process. 53 (4), 358–363 (2017)].Google Scholar
  4. 4.
    D. T. Pham and X. Liu, Neural Networks for Identification, Prediction and Control (Springer-Verlag, London, 1995).CrossRefGoogle Scholar
  5. 5.
    R. S. M. Munoz, C. Rossi, and A. B. Cruz, “Modelling and Identification of Flight Dynamics in Mini-Helicopters Using Neural Networks,” in Aerial Vehicles, Ed. by T. M. Lam (InTech, Rijeka, Croatia, 2009), pp. 601–620.Google Scholar
  6. 6.
    S. A. Belokon’, Yu. N. Zolotukhin, A. S. Mal’tsev, et al., “Control of Flight Parameters of a Quadrotor Vehicle Moving over a Given Trajectory,” Avtometriya 48 (5), 32–41 (2012) [Optoelectron., Instrum. Data Process. 48 (5), 454–461 (2012)].Google Scholar
  7. 7.
    J. L. Elman, “Finding Structure in Time,” Cognitive Sci. 14 (2), 179–211 (1990). Scholar
  8. 8.
    R. J. Williams, “Some Observations on the Use of the Extended Kalman Filter as a Recurrent Network Learning Algorithm,” Tech. rep. NU-CCS-92-1 (Northeastern University, College of Computer Science, Boston, 1992). Scholar
  9. 9.
    N. P. Rougier, Neural-Networks (GitHub, Inc.).

Copyright information

© Allerton Press, Inc. 2018

Authors and Affiliations

  • Yu. N. Zolotukhin
    • 1
  • K. Yu. Kotov
    • 1
    Email author
  • A. M. Svitova
    • 1
  • E. D. Semenyuk
    • 1
  • M. A. Sobolev
    • 1
  1. 1.Institute of Automation and Electrometry, Siberian BranchRussian Academy of SciencesNovosibirskRussia

Personalised recommendations