DOI 10.17586/0021-3454-2022-65-7-492-499
UDC 681.5.015
IDENTIFICATION OF NON-STATIONARY PARAMETERS OF A LINEAR REGRESSION MODEL UNDER ADDITIVE INFLUENCE OF THE UNMEASURABLE SINUSOIDAL DISTURBANCE
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
A. V. Kaplin
ITMO University, Faculty of Control Systems and Robotics;
N. A. Nikolaev
ITMO University, Saint Petersburg, 197101, Russian Federation; Associate professor
O. V. Oskina
ITMO University, Saint Petersburg, 197101, Russian Federation; Student, Engineer
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Abstract. In the framework of the deterministic approach, an algorithm for identifying nonstationary parameters for the classical linear regression equation is proposed. When synthesizing the algorithm for estimating non-stationary parameters, it is assumed that the dynamic model of their variation is known and is a linear generator with variable coefficients. An additional complication of the problem of estimating parameters for the linear regression equation is the presence of an additive sinusoidal disturbing effect with unknown constant amplitudes, frequencies, and phases. The resulting algorithm provides an accurate estimation of all unknown non-stationary parameters.
Keywords: linear regression equation, parameters estimation, time-varying parameters, sinusoidal disturbance
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