COMPARATIVE ANALYSIS OF ALGORITHMS FOR OBJECTS WITH NONLINEAR DYNAMICS IDENTIFICATION

 

UDC 681.53

 

Oliferovich Nadezhda Mikhaylovna – Senior Lecturer, the Department of Automation of Production Processes and Electrical Engineering. Belarusian State Technological University (13a, Sverdlova str., 220006, Minsk, Republic of Belarus). E-mail: oliferovich@belstu.by

Hryniuk Dzmitry Anatol’yevich – PhD (Engineering), Associate Professor, Assistant Professor, the Department of Automation of Production Processes and Electrical Engineering. Belarusian State Technological University (13a, Sverdlova str., 220006, Minsk, Republic of Belarus). E-mail: hryniukda@gmail.com

Bazarbaev Qabilbek Amanlyl uly – Master’s degree student the Department of Automation of Production Processes and Electrical Engineering. Belarusian State Technological University (13a, Sverdlova str., 220006, Minsk, Republic of Belarus). kabulbazarbaev@gmail.com

Orobei Igor Olegovich – PhD (Engineering), Associate Professor, Assistant Professor, the Department of Automation of Production Processes and Electrical Engineering. Belarusian State Technological University (13a, Sverdlova str., 220006, Minsk, Republic of Belarus). E-mail: orobei@gmail.com.

Suhorukova Irina Gennad’yevna – Senior Lecturer, the Department of Software Engineering. Belarusian State Technological University (13a, Sverdlova str., 220006, Minsk, Republic of Belarus). E-mail: irina_x@rambler.ru

 

DOI: https://doi.org/ 10.52065/2520- 6141-2023-272-2-11 (In Russian).

 

Key words: nonlinear dynamics, identification, heat exchanger dynamics.

 

For citation: Oliferovich N. M., Hryniuk D. A., Bazarbaev Q. A., Orobei I. O., Suhorukova I. G. Comparative analysis of algorithms for objects with nonlinear dynamics identification. Proceedings of BSTU, issue 3, Physics and Mathematics. Informatics, 2023, no. 2 (272), pp. 71–79. DOI: 10.52065/2520- 6141-2023-272-2-11 (In Russian).

 

Abstract

The article compares different approaches to active identification under the conditions of an explicit manifestation of the nonlinear properties of a thermal object and measuring instruments. The studies considered the choice of methods that are suitable for identifying dynamics in real time. The object identification was carried out by three methods. The first method involves the continuous formation of a meander signal. The period of the meander signal was changed in order to identify its minimum value to ensure stable identification. To obtain the parameters of the transfer function, the method of minimizing transient processes for several periods was used. Since the object exhibited characteristic fluctuations during the period of one meander, compensation was made by approximating the quadratic dependence using the least squares method. This decision made it possible to provide a smaller variation in the identification results during the observation time. As the second and third methods, frequency identification of the transfer function parameters was used by forming four signals of different frequencies, which are not multiple harmonics. In the second case, the signals were harmonic with amplitudes that are approximately inverse to the transfer coefficients of the frequency response of the object of study. The third option involved the use of signals of the same frequency, but of a rectangular shape. Based on the results of the obtained frequency response, the parameters of the transfer function were determined. Compensation of long-term trends has improved the quality of identification. The use of rectangular signals provided a greater number of points on the amplitude characteristic and the stability of determining the dynamics parameters.

 

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References

1.Lennart L. System Identification: Theory for the User. Prentice-Hall, Upper Saddle River, New Jersey, Prentice Hall PTR Publ., 1999. 609 p.

  1. Niu S., Fisher DG., Xiao D. An augmented UD identification algorithm. International Journal of Control., 1992, vol. 56, issue 1, pp. 193–211.
  2. Mikles J., Fikar M. Process Modelling, identification, and control. Berlin, Heidelberg, SpringerVerlag Publ., 2007. 497 p.
  3. Lobaty A. A., Stepanov V. Y. Parametric identification of stochastic system by non-gradient random searching. Nauka i Тekhnika [Science & Technique], 2017, no. 16 (3), pp. 256–261. DOI: 10.21122/2227- 1031-2017-16-3-256-261 (In Russian).
  4. Niu S. Process Control Engineering Analyses. Best Practices, 2022. 501 p.
  5. Balakirev V. S., Dudnikov E. G., Tsirlin A. M. Eksperimental'noye opredeleniye dinamicheskikh kharakteristik promyshlennykh ob"ektov upravleniya [Experimental determination of the dynamic characteristics of industrial control objects]. Moscow, Energiya Publ., 1967. 232 p. (In Russian).
  6. Koplyarova N. V., Sergeeva N. A. Nonparametric algorithms of Wiener and Hammerstein type systems identification. Sistemy upravleniya i informatsionnyye tekhnologii [Control systems and information technologies], 2013, no. 2 (52), pp. 133–137 (In Russian).
  7. Pupkova K. A., Egupova N. D. Metody klassicheskoy i sovremennoy teorii avtomaticheskogo upravleniya [Methods of classical and modern theory of automatic control]. Moscow, the Bauman University Publ., 2004. 656 p. (In Russian).
  8. Keesman K. J. System identification. An introduction. London, Springer Publ., 2011. 351 p.
  9. Corriou JP. Process control – theory and applications. 2nd ed. Springer Publ., 2017. 866 p.
  10. Nelles O. Nonlinear system identification: from classical approaches to neural networks. SpringerVerlag Publ., 2001. 785 p.
  11. Billings S. A. Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. Wiley, 2013. 576 p.
  12. Joanofarc X., Patnaik S., Panda R.Process Modeling, identification methods, and control schemes for nonlinear physical systems – a comprehensive review. ChemBioEng Reviews, 2021, vol. 8, issue 4, pp. 1–21. DOI:10.1002/cben.202000017.
  13. Oliferovich N. M., Hryniuk D. A., Orobei I. O. Harmonic identification of technological objects in real time. Trudy BGTU [Proceedings of BSTU], 2016, no. 6: Physics and Mathematics. Informatics, pp. 117– 121 (In Russian).
  14. Oliferovich N., Hryniuk D., I. Orobei. Harmonic identification of technological objects in real time. Electrical, Electronic and Information Sciences (eStream): Open Conference. Vilnius, 2016, pp. 1–4. DOI: 10.1109/eStream39242.2016.7485915.
  15. Oliferovich N. M., Hryniuk D. A., Orobei I. O. Harmonic identification algorithms for technological objects and their approbation on a thermal object. Trudy BGTU [Proceedings of BSTU], 2017, no. 2: Physics and Mathematics. Informatics, pp. 76–81 (In Russian).
  16. Oliferovich N., Hryniuk D., I. Orobei. The use of harmonic identification algorithms to air heat exchanger. Electrical, Electronic and Information Sciences (eStream): Open Conference. Vilnius, 2017, pp. 1–5. DOI: 10.1109/eStream.2017.7950326.
  17. Markov A. V., Simankov V. I. Parametric identification of dynamic objects via phase-frequency characteristicS. Doklady BGUIR [[Proceedings of BSUIR], 2015, no. 3, pp. 29–35 (In Russian).
  18. Marozava M., Hryniuk D. Experimental study of the variation dynamic’s for air heat exchanger. Science – Future of Lithuania Elektronika ir elektrotechnika Electronics and Electrical Engineering, 2017, vol. 9, issue 3, pp. 297–301.
  19. Hryniuk D. A., Oliferovich N. M., Suhorukova I. G., Orobei I. O. Identification of the dynamic channels parameter's for the airflow heat exchanger. Trudy BGTU [Proceedings of BSTU], issue 3, Physics and Mathematics. Informatics, 2021, no. 2 (260), pp. 70–79 (In Russian).
  20. Hryniuk D., Suhorukova I., Oliferovich N. Adaptive smoothing and filtering in transducers. Electrical, Electronic and Information Sciences (eStream): Open Conference. Vilnius, 2016, pp. 1–4. DOI: 10.1109/eStream39242.2016.7485917.
  21. Hryniuk D. A., Oliferovich N. M., Suhorukova I. G. Method of PID-controller tuning through deadbeat-regulator for various integral criteria. Trudy BGTU [Proceedings of BSTU], issue 3, Physics and Mathematics. Informatics, 2019, no. 2 (224), pp. 66–73 (In Russian).
  22. Hryniuk D. A., Oliferovich N. M., Suhorukova I. G., Orobei I. O. Modeling and tuning control objects with nonlinear dynamics. Trudy BGTU [Proceedings of BSTU], issue 3, Physics and Mathematics. Informatics, 2021, no. 2 (248), pp. 65–71 (In Russian).

 

Поступила после доработки 15.06.2023