Skip to main content
Log in

Identification of Dynamic Objects using a Family of Experimental Supporting Integral Curves

  • Analysis and Synthesis of Signals and Images
  • Published:
Optoelectronics, Instrumentation and Data Processing Aims and scope

Abstract

A specially planned experiment based on obtaining a required family of estimates of supporting integral curves (approximately described in a given finite system of base functions) is used to solve a problem of active identification of a dynamic object, which corresponds to an a priori unknown differential equation. In view of the fact that experimental data may contain fluctuation and singular interference, a method is developed for optimal unbiased estimation of linear quantitative characteristics of the object behavior and an approximate analytical solution (differential equation), which is valid for a given set of permitted time values and an initial condition. The basic characteristics of the method are substantiated, and the results of the computational experiment are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. A. A. Krasovskii, “Science Studies and the State of the Theory of Control Processes. Overview,” Avtomatika i Telemekhanika, No. 4, 3–19 (2000).

    Google Scholar 

  2. V. N. Brandin, A. A. Vasil’ev, and S. T. Khudyakov, Fundamentals of Experimental Space Ballistics (Mashinostroenie, Moscow, 1974) [in Russian].

    Google Scholar 

  3. L. Ljung, “Model Accuracy in System identification,” Tekhnicheskaya Kibernetika, No. 6, 55–64 (1992) [IFAC Proceedings Volumes 24 (1), 277–281 (1991)].

    MATH  Google Scholar 

  4. Yu. G. Bulychev and A. P. Manin, Mathematical Aspects in Determining the Motion of Aerocrafts (Mashinostroene, Moscow, 2000) [in Russian].

    Google Scholar 

  5. Yu. G. Bulychev, V. V. Vasil’ev, R. V. Dzhugan, et al., Information-Measurement Software of Field Tests of Complex Technical Units, Ed. by A. P. Manin and V. V. Vasil’ev (Mashinostroenie-Polyot, Moscow, 2016) [in Russian].

  6. V. A. Leonov and B. K. Poplavskii, “ Filtering the Measurement Errors in Estimating the Linear Transformation of a Useful Signal,” Tekhnicheskaya Kibernetika, No. 1, 163–170 (1992).

    Google Scholar 

  7. Yu. G. Bulychev and A. V. Eliseev, “Measurement Processing Under Multistructural Interferences,” Avtometriya 43 (5), 26–38 (2007) [Optoelectron., Instrum. Data Process. 43 (5), 408–418 (2007)].

    Google Scholar 

  8. Yu. G. Bulychev, L. I. Borodin, V. A. Golovskoi, et al., “Processing Measured Data with a Random Change in the Structure of Dynamic Interferences,” Avtometriya 45 (2), 14–21 (2009) [Optoelectron., Instrum. Data Process. 45 (2), 100–106 (2009)].

    Google Scholar 

  9. Yu. G. Bulychev, “Methods for Numerical-Analytical Integration of Differential Equations,” Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki 31 (9), 1305–1319 (1991).

    MathSciNet  MATH  Google Scholar 

  10. Yu. G. Bulychev, A. A. Manin, and A. G. Zhukovskii, “Experimental-Analytical Method for Synthesizing Mathematical Models of Uncontrollable Motion of Spacecrafts,” Kosmicheskie Issledovaniya 37 (3), 312–321 (1999).

    Google Scholar 

  11. V. V. Ivanov, Calculation on an ECM (Nauk. Dumka, Kiev, 1986) [in Russian].

    Google Scholar 

  12. K. I. Babenko, Fundamentals of Numerical Analysis (Nauka, Moscow, 1986) [in Russian].

    Google Scholar 

  13. N. S. Bakhvalov, N. P. Zhidkov, G. M. Kobel’kov, Numerical Methods (BINOM. Laboratoriya Znanii, Moscow, 2008) [in Russian].

    MATH  Google Scholar 

  14. A. I. Egorov, Riccati Equations (Pensoft Publishers, 2007).

    Google Scholar 

  15. E. Kamke, Handbook on Ordinary Differential Equations (Nauka, Moscow, 1971) [in Russian].

    Google Scholar 

  16. Yu. G. Bulychev and E. N. Chepel’, “Quasioptimal Method for Solving the Triangulation Problem in Prior Uncertainty,” Avtometriya 53 (6), 83–91 (2017) [Optoelectron., Instrum. Data Process. 53 (6), 604–611 (2017)].

    Google Scholar 

  17. Yu. G. Bulychev, I. G. Nasenkov, and A. V. Yachmenev, “Amplitude-Hyperbolic Method of Passive Location of a Radiation Source,” Avtometriya 54 (4), 43–50 (2018) [Optoelectron., Instrum. Data Process. 54 (4), 355–360 (2018)].

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yu. G. Bulychev.

Additional information

Russian Text © Yu.G. Bulychev, A.G. Kondrashov, P.Yu. Radu, 2019, published in Avtometriya, 2019, Vol. 55, No. 1, pp. 98–110.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bulychev, Y.G., Kondrashov, A.G. & Radu, P.Y. Identification of Dynamic Objects using a Family of Experimental Supporting Integral Curves. Optoelectron.Instrument.Proc. 55, 81–92 (2019). https://doi.org/10.3103/S8756699019010138

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S8756699019010138

Keywords

Navigation