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Bibliographic Details
Main Authors: Glushchenko, Anton, Lastochkin, Konstantin
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2403.13664
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author Glushchenko, Anton
Lastochkin, Konstantin
author_facet Glushchenko, Anton
Lastochkin, Konstantin
contents A new adaptive observer is proposed for a certain class of nonlinear systems with bounded unknown input and parametric uncertainty. Unlike most existing solutions, the proposed approach ensures asymptotic convergence of the unknown parameters, state and perturbation estimates to an arbitrarily small neighborhood of the equilibrium point. The solution is based on the novel augmentation of a high-gain observer with the dynamic regressor extension and mixing (DREM) procedure enhanced with a perturbation annihilation algorithm. The aforementioned properties of the proposed solution are verified via numerical experiments.
format Preprint
id arxiv_https___arxiv_org_abs_2403_13664
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Adaptive Reconstruction of Nonlinear Systems States via DREM with Perturbation Annihilation
Glushchenko, Anton
Lastochkin, Konstantin
Systems and Control
A new adaptive observer is proposed for a certain class of nonlinear systems with bounded unknown input and parametric uncertainty. Unlike most existing solutions, the proposed approach ensures asymptotic convergence of the unknown parameters, state and perturbation estimates to an arbitrarily small neighborhood of the equilibrium point. The solution is based on the novel augmentation of a high-gain observer with the dynamic regressor extension and mixing (DREM) procedure enhanced with a perturbation annihilation algorithm. The aforementioned properties of the proposed solution are verified via numerical experiments.
title Adaptive Reconstruction of Nonlinear Systems States via DREM with Perturbation Annihilation
topic Systems and Control
url https://arxiv.org/abs/2403.13664