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vol 67 / April, 2024
Article

DOI 10.17586/0021-3454-2023-66-8-627-636

UDC 681.51

ADAPTIVE OBSERVER OF STATE VARIABLES OF A NONLINEAR NONSTATIONARY SYSTEM WITH UNKNOWN CONSTANT PARAMETERS

O. A. Kozachek
ITMO University, Saint Petersburg, 197101, Russian Federation; Engineer


A. A. Bobtsov
ITMO University, Saint Petersburg, 197101, Russian Federation; Head of the School of Computer Technologies and Control, Professor at the Faculty of Control Systems and Robotics, Head of the Adaptive and Nonlinear Control Systems Lab


N. A. Nikolaev
ITMO University, Saint Petersburg, 197101, Russian Federation; Associate professor


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Reference for citation: Kozachek О. А., Bobtsov А. А., Nikolaev N. А. Adaptive observer of state variables of a nonlinear nonstationary system with unknown constant parameters. Journal of Instrument Engineering. 2023. Vol. 66, N 8. P. 627—636 (in Russian). DOI: 10.17586/0021-3454-2023-66-8-627-636.

Abstract. For a nonlinear nonstationary system an adaptive state vector observer using output variable measurement is developed the control matrix (vector) and the nonlinear component of the equation of state of the system contain unknown constant parameters. When synthesizing the observer, a preliminary parametrization of the original nonlinear system is carried out. Then the derived system is reduced to a linear regression model. At the next stage, unknown constant regression parameters are estimated using the least squares method with a forgetting factor. The result of the previous work by the authors, which considered a linear non-stationary system containing unknown parameters in the control matrix (vector), is extended to the case when the equation of state of the system contains a partially unknown nonlinearity. The performance of the proposed algorithm is illustrated by mathematical modeling.
Keywords: adaptive observer, nonlinear system, nonstationary system, linear regression model, parameters identification

Acknowledgement: The article was prepared with the financial support of the Russian Science Foundation, grant 22-21-00499.

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