Non-asymptotic numerical differentiation: a kernel-based approach
File(s)
Author(s)
Li, P
Pin, G
Fedele, G
Parisini, T
Type
Journal Article
Abstract
The derivative estimation problem is addressed in this paper by using Volterra integral 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 estimation 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.
Date Issued
2018-06-28
Date Acceptance
2018-05-14
ISSN
0020-7179
Publisher
Taylor & Francis
Start Page
2090
End Page
2099
Journal / Book Title
International Journal of Control
Volume
91
Issue
9
Copyright Statement
© 2018 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Control on 8 June 2018, available online: https://dx.doi.org/10.1080/00207179.2018.1478130
Source Database
manual-entry
Subjects
Science & Technology
Technology
Automation & Control Systems
Linear integral operators
numerical differentiation
non-asymptotic identification
state estimation
Fredholm-Volterra integral equations
Industrial Engineering & Automation
0102 Applied Mathematics
0906 Electrical and Electronic Engineering
Publication Status
Published
Date Publish Online
2018-06-08