Preview

Mekhatronika, Avtomatizatsiya, Upravlenie

Advanced search

Algorithm for Identifi cation of Parameters Sinusoidal Signal with the Exponentially Damping Amplitude

https://doi.org/10.17587/mau.23.125-131

Abstract

The paper proposes a new method for estimating the parameters of an unbiased sinusoidal signal with the exponentially damping amplitude: frequency, damping coefficient. A sinusoidal signal with exponentially damping amplitude is an important class that can be observed in a wide range of natural phenomena, such as the propagation of acoustic waves, and can also characterize the behavior of artificial systems, arising, for example, as a result of complex interactions between the components of power systems, therefore the task of estimating parameters is sinusoidal. signal with exponentially decaying amplitude is relevant at the present time. It is assumed that the phase, frequency, damping factor and amplitude of a sinusoidal signal with exponentially decaying amplitude are unknown functions of time. In the present work, a new method is proposed for parameterizing a sinusoidal signal with exponentially decaying amplitude. First, a sinusoidal signal with exponentially decaying amplitude is presented as the output of a linear generator, the parameters of the decaying sinusoidal signal (amplitude, phase, damping factor and frequency) are unknown. Then the Jordan form of the matrix and the delay are applied to transform the measured signal, then a linear regression model is obtained, which depends on the frequency and the attenuation coefficient. At the last stage, unknown parameters (frequency, attenuation coefficient) are calculated from the obtained linear regression model. Numerical modeling demonstrates the effectiveness of the proposed methodology.

About the Authors

Nguyen Khac Tung
ITMO University
Russian Federation

Nguyen Khac Tung, Postgraduate Student

 St. Petersburg



S. M. Vlasov
ITMO University
Russian Federation

 St. Petersburg



A. A. Pyrkin
ITMO University
Russian Federation

 St. Petersburg



References

1. Pyrkin A. A., Bobtsov A. A., Vedyakov A. A., Kolyubin S. A. Estimation of parameters of a polyharmonic signal, Automation and Telemecha nics, 2015, no. 8, pp. 94—114 (in Russian).

2. Aranovskiy S., Bobtsov A., Kremlev A., Nikolaev N., Slita O. Identification of frequency of biased harmonic signal, European Journal of Control, 2010, vol. 16, no. 2, pp. 129—139, DOI: 10.3166/ejc.16.129-139.

3. Marino R., Tomei P. Frequency estimation of periodic signals, Proc. European Control Conference, Strasbourg, France, 2014, pp. 7—12, DOI: 10.1109/ecc.2014.

4. Hou M. Parameter identification of sinusoids, IEEE Transactions on Automatic Control, 2012, vol. 57, no. 2, pp. 467—472, DOI: 10.1109/TAC.2011.2164736.

5. Khac T., Vlasov S. M., Iureva R. A. Estimating the Frequency of the Sinusoidal Signal using the Parameterization based on the Delay Operators, ICINCO 2021 — Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics, 2021, pp. 656—660.

6. Vlasov S. M., Margun A. A., Kirsanova A. S., Vakhvianova P. D. Adaptive controller for uncertain multi-agent system under disturbances, ICINCO 2019 — 16th International Conference on Informatics in Control, Automation and Robotics, 2019, vol. 2, pp. 198—205.

7. Vlasov S. M., Kirsanova A. S., Dobriborsci D., Borisov O. I., Gromov V. S., Pyrkin A. A., Maltsev M. V., Semenev A. N. Output Adaptive Controller Design for Robotic Vessel with Parametric and Functional Uncertainties, 26th Mediterranean Conference on Control and Automation, MED 2018, 2018, pp. 547—552.

8. Sevasteeva E., Chernov V., Bobtsov A. Algorithm for increasing the speed of identification of the frequency of a sinusoidal signal, Izv. universities. Instrumentation, 2019, vol. 62, no. 9, pp. 767—771 (in Russian).

9. Bobtsov A., Lyamin A., Romasheva D. Algorithm of parameter’s identification of polyharmonic function, IFAC Proceedings Volumes, 2002, vol. 35, no. 1, pp. 439—443.

10. Marino R., Tomei R. Global Estimation of Unknown Frequencies, IEEE Transactions on Automatic Control, 2002, vol. 47, pp. 1324—1328.

11. Osborne M., Smyth G. K. A modified Prony algorithm for fitting functions defined by difference equations, SIAM Journal on Scientific and St atistical Computing, 1991, vol. 12, no. 2, pp. 362—382.

12. Osborne M., Smyth G. K. A modified prony algorithm for exponential function fitting, SIAM Journal on Scientific Computing, 1995, vol. 16, no. 1, pp. 119—138.

13. Jin Lu, Brown Lyndon J. Identification of Exponentially Damped Sinusoidal Signals, IFAC Proceedings Volumes, 2008, vol. 41, iss. 2.

14. Vediakova A., Vedyakov A., Bobtsov A., Pyrkin A. DREM-based Parametric Estimation of Bias-affected Damped Sinusoidal Signals*, 2020 European Control Conference (ECC), 2020, pp. 214—219, DOI: 10.23919/ECC51009.2020.9143821

15. Wang Y., Chen B., Pin G., Parisini T. Estimation of damped sinusoidal signals: an observer-based approach, IFACPapersOnLine, 2017, vol. 50, no. 1, pp. 3811—3816.

16. Nikiforov V. O. Adaptive servomechanism controller with an implicit reference model, Intern. Journal of Control, 1997, vol. 68, no. 2, pp. 277—286.

17. Umari Amjad M. J., Gorelick Steven M. Evaluation of the matrix exponential for in ground-water-flow and solute-transport simulation: theoretical framework, U. S. Geological Survey, 1986, DOI 10.3133/wri864096.

18. Aranov skiy S., Bobtsov A., Ortega R., Pyrkin A. Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing*, IEEE Transactions on Automatic Control, July 2017, vol. 62, no. 7, pp. 3546—3550, DOI: 10.1109/ TAC.2016.2614889.

19. Aranovskiy S., Bobtsov A., Ortega R., Pyrkin A. Parameters estimation via dynamic regressor extension and mixing, 2016 American Control Conference (ACC), 2016, pp. 6971—6976, DOI: 10.1109/ACC.2016.7526771.


Review

For citations:


Tung N., Vlasov S.M., Pyrkin A.A. Algorithm for Identifi cation of Parameters Sinusoidal Signal with the Exponentially Damping Amplitude. Mekhatronika, Avtomatizatsiya, Upravlenie. 2022;23(3):125-131. (In Russ.) https://doi.org/10.17587/mau.23.125-131

Views: 402


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1684-6427 (Print)
ISSN 2619-1253 (Online)