Mathematical modeling of parameter identification process of convection-diffusion transport models using the SVD-based Kalman filter

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Abstract

The paper addresses a problem of mathematical modeling of the process of identifying the coefficients of a partial differential equation in convection-diffusion transport models based on the results of noisy measurements of the function values. Identification process is performed using a new method belonging to the class of recurrent parameter identification methods based on optimal discrete Kalman-type filtering algorithms. One-dimensional models with constant coefficients, boundary conditions of first kind, or mixed boundary conditions of first and third kind are considered.

The proposed method is based on the transition from the initial continuous model with a partial differential equation to the model described by the state-space linear discrete-time dynamic system and the application of the maximum likelihood method to it with construction of an identification criterion (likelihood function) based on the values calculated by the SVD algorithm of the Kalman filtering. This filter is based on the singular value decomposition of error covariance matrix and works stably even in cases when it is close to singular. The SVD filter has proven itself well in solving various problems of discrete filtering and parameter identification. It has several advantages over the traditionally used conventional Kalman filter. The main of which is robustness against machine roundoff errors.

Computer modeling of parameter identification has been processed with the MATLAB system using a specialized software package. The results of numerical experiments confirm the efficiency of the proposed method and its advantages compared to the similar one based on the conventional Kalman filter.

About the authors

Anastasia N. Kuvshinova

Ilya Ulyanov State Pedagogical University

Email: kuvanulspu@yandex.ru
ORCID iD: 0000-0002-3496-5981
SPIN-code: 2849-0643
Scopus Author ID: 57204965949
http://www.mathnet.ru/person141068

Postgraduate Student; Dept. of Higher Mathematics

4/5, Lenin Square, Ulyanovsk, 432071, Russian Federation

Andrey V. Tsyganov

Ilya Ulyanov State Pedagogical University

Email: andrew.tsyganov@gmail.com
ORCID iD: 0000-0002-4173-5199
SPIN-code: 2729-7659
Scopus Author ID: 35186570100
ResearcherId: C-2331-2014
http://www.mathnet.ru/person178940

Cand. Phys. & Math. Sci; Professor; Dept. of Higher Mathematics

4/5, Lenin Square, Ulyanovsk, 432071, Russian Federation

Yulia V. Tsyganova

Ulyanovsk State University

Author for correspondence.
Email: tsyganovajv@gmail.com
ORCID iD: 0000-0001-8812-6035
SPIN-code: 8259-4594
Scopus Author ID: 6507218923
ResearcherId: F-7169-2013
http://www.mathnet.ru/person69680

Dr. Phys. & Math. Sci.; Professor; Dept. of Information Technology

42, L. Tolstoy st., Ulyanovsk, 432017, Russian Federation

References

  1. Leont’ev A. I., Kozhinov I. A., Isaev S. I., et al. Teoriia teplomassoobmena [Theory of Heat and Mass Transfer]. Moscow, Bauman Moscow State Techn. Univ., 2018, 462 pp. (In Russian)
  2. Farlow S. J. Partial Differential Equations for Scientists and Engineers. New York, Dover Publ., 1982, ix+414 pp.
  3. Denisov A. M. Vvedenie v teoriiu obratnykh zadach [Introduction to the Theory of Inverse Problems]. Moscow, Moscow State Univ., 1994, 208 pp. (In Russian)
  4. Matsevityi Yu. M., Multanovskii A. V. Identification of heat transfer parameters by the method of optimal dynamic filtering, High Temperature, 1979, vol. 17, no. 5, pp. 1053–1060 (In Russian).
  5. Karpov A. A., Tikhonova T. A. Recovery of non-stationary heat flows from experimental data, Matem. Mod., 2000, vol. 12, no. 5, pp. 101–106 (In Russian).
  6. Simbirskiy G. D., Lantrat V. K. Application of the Kalman digital filter for parametric identification high-temperature thermocouple, Autom. Electron. Modern Technology, 2017, no. 11, pp. 68–75 (In Russian).
  7. Pilipenko N. V. Primenenie fil’tra Kalmana v nestatsionarnoi teplometrii [Applying the Kalman Filter in Non-Stationary Heat Metering]. St. Petersburg, ITMO Univ., 2017, 36 pp. (In Russian)
  8. Matveev M. G., Kopytin A. V., Sirota E. A. Combined method for identifying the parameters of a distributed dynamic model, In: Proc. IV Int. Conf. (ITNT, 2018). Samara, 2018, pp. 1651–1657 (In Russian).
  9. Pilipenko N. V., Zarichnyak Yu. P., Ivanov V. A., Khalyavin A. M. Parametric identification of differencial-difference models of heat transfer in one-dimensional bodies based on Kalman filter algorithms, Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2020, vol. 20, no. 4, pp. 584–588 (In Russian). https://doi.org/10.17586/2226-1494-2020-20-4-584-588.
  10. Grewal M. S., Andrews A. P. Kalman Filtering. Theory and Practice with MATLAB. Hoboken, NJ, John Wiley and Sons, 2015, xvii+617 pp. https://doi.org/10.1002/9781118984987.
  11. Tsyganov A. V., Tsyganova Yu. V., Kuvshinova A. N., Tapia Garza H. R. Metaheuristic algorithms for identification of the convection velocity in the convection-diffusion transport model, In: CEUR Workshop Proceedings, vol. 2258, 2018, pp. 188–196. http://ceur-ws.org/Vol-2258/paper24.pdf.
  12. Kuvshinova A. N. Dynamic identification of boundary conditions for convection-diffusion transport model in the case of noisy measurements, Zhurnal SVMO, 2019, vol. 21, no. 4, pp. 469–479 (In Russian). https://doi.org/10.15507/2079-6900.21.201904.469-479.
  13. Kuvshinova A. N., Tsyganov A. V., Tsyganova Yu. V., Tapia Garza H. R. Parameter identification algorithm for convection-diffusion transport model, J. Phys.: Conf. Ser., 2021, vol. 1745, 012110. https://doi.org/10.1088/1742-6596/1745/1/012110.
  14. Fomin V. N. Rekurrentnoe otsenivanie i adaptivnaia fil’tratsiia [Recurrent Estimation and Adaptive Filtering]. Moscow, Nauka, 1984, 288 pp. (In Russian)
  15. Maybeck P. S. Stochastic Models, Estimation, and Control, vol. 1, Mathematics in Science and Engineering, vol. 141. New York, San Francisco, London, Academic Press, 1979, xix+423 pp.
  16. Kuvshinova A. N. Analysis of discrete linear stochastic model of convection-diffusion transport, Uchenye Zapiski UlGU. Ser. Matem. Inform. Tekhn., 2019, no. 1, pp. 65–69 (In Russian).
  17. Åström K. J. Maximum likelihood and prediction error methods, Automatica, 1980, vol. 16, no. 5, pp. 551–574. https://doi.org/10.1016/0005-1098(80)90078-3.
  18. Vasil’ev V. P. Chislennye metody resheniia ekstremal’nykh zadach [Numerical Methods for Solving Extreme Problems]. Moscow, Mir, 1982, 520 pp. (In Russian)
  19. Tsyganova Yu. V., Kulikova M. V. On modern array algorithms for optimal discrete filtering, Vestn. YuUrGU. Ser. Mat. Model. Progr., 2018, vol. 11, no. 4, pp. 5–30 (In Russian). https://doi.org/10.14529/mmp180401.
  20. Björck Å. Numerical Methods in Matrix Computations, Texts in Applied Mathematics, vol. 59. Cham, Springer, 2015, xvi+800 pp. https://doi.org/10.1007/978-3-319-05089-8.
  21. Oshman Y., Bar-Itzhack I. Y. Square root filtering via covariance and information eigenfactors, Automatica, 1986, vol. 22, no. 5, pp. 599–604. https://doi.org/10.1016/0005-1098(86)90070-1.
  22. Oshman Y. Square root information filtering using the covariance spectral decomposition, In: Proc. of the 27th Conf. on Decision and Control, 1988, pp. 382–387. https://doi.org/10.1109/CDC.1988.194335.
  23. Oshman Y. Maximum likelihood state and parameter estimation via derivatives of the V-Lambda filter, J. Guid. Control Dyn., 1992, vol. 15, no. 3, pp. 717–726. https://doi.org/10.2514/3.20896.
  24. Wang L., Libert G., Manneback P. Kalman filter algorithm based on singular value decomposition, In: Proc. of the 31st Conf. on Decision and Control, 1992, pp. 1224–1229. https://doi.org/10.1109/IECON.1992.254406.
  25. Zhang Y., Dai G., Zhang H., Li Q. A SVD-based extended Kalman filter and applications to aircraft flight state and parameter estimation, In: Proc. of 1994 American Control Conf., 1994, pp. 1809–1813. https://doi.org/10.1109/ACC.1994.752384.
  26. Kulikova M. V., Tsyganova J. V. Improved discrete-time Kalman filtering within singular value decomposition, IET Control Theory Appl., 2017, vol. 11, no. 15, pp. 2412–2418, arXiv: 1611.03686 [math.OC]. https://doi.org/10.1049/iet-cta.2016.1282.
  27. Tsyganova J. V., Kulikova M. V. SVD-based Kalman filter derivative computation, IEEE Trans. Autom. Control, 2017, vol. 62, no. 9, pp. 4869–4875, arXiv: 1612.04777 [cs.SY]. https://doi.org/10.1109/TAC.2017.2694350.
  28. Alessandrini M., Biagetti G., Crippa P., Falaschetti L., Manoni L., Turchetti C. Singular value decomposition in embedded systems based on ARM Cortex-M architecture, Electronics, 2021, vol. 10, no. 1, 34. https://doi.org/10.3390/electronics10010034.

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