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Non-Asymptotic Numerical Differentiation: a Kernel-Based Approach

P. Li
•
G. Pin
•
G. Fedele
•
T. Parisini
2018
  • journal article

Periodico
INTERNATIONAL JOURNAL OF CONTROL
Abstract
The derivative estimation problem is addressed in this paper by using Volterra in- tegral operators which allow to obtain the estimates of the time-derivatives with fast convergence rate. A deadbeat state observer is used to provide the estimates of the derivatives with a given fixed-time convergence. The estimation bias caused by modeling error is characterized herein as well as the ISS property of the estima- tion error with respect to the measurement perturbation. A number of numerical examples are carried out to show the effectiveness of the proposed differentiator also including comparisons with some existing methods.
DOI
10.1080/00207179.2018.1478130
WOS
WOS:000442004800011
Archivio
http://hdl.handle.net/11368/2925336
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85049150141
https://www.tandfonline.com/doi/full/10.1080/00207179.2018.1478130
Diritti
open access
license:digital rights management non definito
license:copyright editore
FVG url
https://arts.units.it/request-item?handle=11368/2925336
Soggetti
  • Integral operator

  • derivative estimation...

Web of Science© citazioni
6
Data di acquisizione
Mar 20, 2024
Visualizzazioni
5
Data di acquisizione
Apr 19, 2024
Vedi dettagli
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