Year: 2025
Type: article
Abstract: The constant parameter identification problem is considered for a linear regression model assuming that the noise is sufficiently small comparing to the regressor. With the aim to attenuate the influence of the disturbance, two nonlinear transformations (filters) are proposed, and the estimation is performed for an extended regression dependent on the powers of the unknown parameters and the diminished disturbance. It is shown that such a transformation preserves the excitation of regressor under reasonable assumptions. The quality improvement is demonstrated in numerical experiments.
Source: Journal of Control and Decision
Authors: J. Wang, A. Bobtsov, S. Aranovskiy, D. Efimov, A. Pyrkin
Institutions: Hangzhou Dianzi University, ITMO University, Institut d'Électronique et des Technologies du numéRique, CentraleSupélec, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université de Lille, Centre de Recherche en Informatique, Signal et Automatique de Lille, VALSE: Contrôle en temps fini et estimation pour des systèmes distribués
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4406629891