Exact Asymptotic Estimation of Unknown Parameters of Perturbed LRE with Application to State Observation

Fuente: arXiv
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Main Authors: Glushchenko, Anton, Lastochkin, Konstantin
Format: Preprint
Published: 2023
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author Glushchenko, Anton
Lastochkin, Konstantin
author_facet Glushchenko, Anton
Lastochkin, Konstantin
contents Most identification laws of unknown parameters of linear regression equations (LRE) ensure only boundedness of a parametric error in the presence of additive perturbations, which is almost always unacceptable for practical scenarios. In this paper, a new identification law is proposed to overcome this drawback and guarantee asymptotic convergence of the unknown parameters estimation error to zero in case the mentioned additive perturbation meets special averaging conditions. Such law is successfully applied to state reconstruction problem. Theoretical results are illustrated by numerical simulations.
format Preprint
id arxiv_https___arxiv_org_abs_2310_14073
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Exact Asymptotic Estimation of Unknown Parameters of Perturbed LRE with Application to State Observation
Glushchenko, Anton
Lastochkin, Konstantin
Systems and Control
Statistics Theory
Most identification laws of unknown parameters of linear regression equations (LRE) ensure only boundedness of a parametric error in the presence of additive perturbations, which is almost always unacceptable for practical scenarios. In this paper, a new identification law is proposed to overcome this drawback and guarantee asymptotic convergence of the unknown parameters estimation error to zero in case the mentioned additive perturbation meets special averaging conditions. Such law is successfully applied to state reconstruction problem. Theoretical results are illustrated by numerical simulations.
title Exact Asymptotic Estimation of Unknown Parameters of Perturbed LRE with Application to State Observation
topic Systems and Control
Statistics Theory
url https://arxiv.org/abs/2310.14073