Advertisement

Synthesis of Intelligent Discrete Algorithms for Estimation with Model Adaptation Based on the Combined Maximum Principle

  • Andrey Kostoglotov
  • Sergey Lazarenko
  • Igor Pugachev
  • Alexey Yachmenov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 874)

Abstract

The paper considers the problem to estimate the parameters of the motion of maneuvering targets. The structure of the filter for estimating the parameters of motion is determined by the mathematical model of the motion. At present time the kinematic models are widely used, but they do not fully correspond to the observed dynamics. This may lead to divergence of the estimation process and failure of the computational procedure. New dynamic filters of the combined maximum principle with the dynamic model of motion possess higher accuracy and stability and smaller amount of computational costs in comparison with common filters. The parametric adaptation of the procedure is carried out using fuzzy logic.

Keywords

Adaptation Combined maximum principle Estimation Mathematical model Lagrange equation of the second kind Fuzzy logic 

References

  1. 1.
    Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation. Wiley, New York (2001)CrossRefGoogle Scholar
  2. 2.
    Li, X.R., Jilkov, V.P.: Survey of maneuvering target tracking. Part I: dynamic models. IEEE Trans. Aerosp. Electron. Syst. 39, 1333–1364 (2003)CrossRefGoogle Scholar
  3. 3.
    Kostoglotov, A.A., Kostoglotov, A.I., Lazarenko, S.V.: Joint maximum principle in the problem of synthesizing an optimal control of nonlinear systems. Autom. Control. Comput. Sci. 41, 274–281 (2007)CrossRefGoogle Scholar
  4. 4.
    Derabkin, I.V., Kostoglotov, A.A., Kuznetcov, A.A., Lazarenko, S.V., Losev, V.A.: The stochastic synthesis of the adaptive filter for estimating the controlled systems state based on the condition of maximum of the generalized power function. In: MATEC Web of Conference, vol. 77, pp. 1–4 (2016)Google Scholar
  5. 5.
    Kostoglotov, A.A., Kuznetcov, A.A., Lazarenko, S.V., Deryabkin, I.V.: The method of structural adaptation of discrete algorithms for the combined maximum principle in problems of estimation of motion parameters. Inf.-Control. Syst. 85, 10–15 (2016)Google Scholar
  6. 6.
    Kostoglotov, A.A., Kuzin, A.P., Lazarenko, S.V., Pugachev, I.V.: The combined maximum principle in the problem of synthesis of an adaptive dynamic filter under conditions of disturbances in the measurement process. In: MATEC Web of Conference, vol. 132, pp. 1–5 (2017)CrossRefGoogle Scholar
  7. 7.
    Kostoglotov, A.A., Lazarenko, S.V.: Synthesis of adaptive tracking systems based on the hypothesis of stationarity of the hamiltonian on the switching hypersurface. J. Commun. Technol. Electron. 62, 123–127 (2017)CrossRefGoogle Scholar
  8. 8.
    Eliseev, A.V., Muzychenko, N.Yu.: Method of adaptive Kalman filter settings in the task of tracking for a dynamic object with unknown acceleration. J. Radio Eng. 8, 39–44 (2014)Google Scholar
  9. 9.
    Muzychenko, N.Yu.: Synthesis of an optimum linear meter for observations in the presence of correlated interferences on the basis of fuzzy-logic algorithms. J. Commun. Technol. Electron. 55, 755–758 (2014)CrossRefGoogle Scholar
  10. 10.
    Kostoglotov, A.A., Lazarenko, S.V., Lyaschenko, Z.V.: Intellectualization of measuring systems based on the method of structural adaptation in the construction of tracking filter. In: Proceedings of 2017 20th IEEE International Conference on Soft Computing and Measurements, Saint Petersburg (2017)Google Scholar
  11. 11.
    Lur’e, A.I., Analiticheskaya Mekhanika (Analytical mechanics). Gos. Izd. Fiz.-Matem. Liter., Moscow (1961)Google Scholar
  12. 12.
    Derabkin, I.V., Kostoglotov, A.A., Kuzin, A.P., Lazarenko, S.V., Manaenkova, O.N., Pugachev, I.V.: Fuzzy control laws in the basis of solutions of synthesis problems of the combined maximum principle. In: Advances in Intelligent Systems and Computing, vol. 679, pp. 322–329 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Rostov State Transport UniversityRostov-on-DonRussian Federation
  2. 2.Don State Technical UniversityRostov-on-DonRussian Federation

Personalised recommendations