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	Method for identification of sinusoidal signal parameters with variable unknown amplitude</div>

Nguyen Khac Tung, Vlasov Sergey M.

2023 , VOLUME 23, NUMBER 0 ( )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

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Editor-in-Chief

Nikiforov
Vladimir O.
D.Sc., Prof.

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doi: 10.17586/2226-1494-2023-23-1-54-61

Method for identification of sinusoidal signal parameters with variable unknown amplitude

K. Nguyen, S. M. Vlasov

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Article in Russian

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Nguyen K.T., Vlasov S.M. Method for identification of sinusoidal signal parameters with variable unknown amplitude. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 1, pp. 54–61 (in Russian). doi: 10.17586/2226-1494-2023-23-1-54-61

Abstract

A new method proposed for identifying the parameters of sinusoidal signal with unknown variable amplitude. The problem of estimating the parameters of sinusoidal signals is relevant in the problems of dynamic positioning and disturbance compensation, for the synthesis of control laws that take into account external disturbances. In the proposed method, the restriction on the signal amplitude is removed. In contrast to known approaches, where the amplitude must be fixed, in the proposed method the signal amplitude can be variable. To implement the proposed identification algorithm, the Jordan matrix form and delay operators are used. During parameterization, a regression model is formed containing unknown stationary parameters. To search for unknown parameters, the method of dynamic expansion of the regressor and mixing is used. The results of computer simulation demonstrate the efficiency of the proposed algorithm. The simulation results confirmed the convergence of parameter estimation to the true values. The proposed approach can be applied to a wide class of applied problems related to disturbance compensation in vibration protection systems, monitoring systems in determining the parameters of high-rise or large-span building structures, and in robotic object control systems.

Keywords: sinusoidal signal, identification, variable amplitude, Jordan form of the matrix, linear regression model

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