Kernel methods for the approximation of discrete-time linear autonomous and control systems
File(s)
Author(s)
Hamzi, Boumediene
Colonius, Fritz
Type
Journal Article
Abstract
Methods from learning theory are used in the state space of linear dynamical and control systems in order to estimate relevant matrices and some relevant quantities such as the topological entropy. An application to stabilization via algebraic Riccati equations is included by viewing a control system as an autonomous system in an extended space of states and control inputs. Kernel methods are the main techniques used in this paper and the approach is illustrated via a series of numerical examples. The advantage of using kernel methods is that they allow to perform function approximation from data and, as illustrated in this paper, allow to approximate linear discrete-time autonomous and control systems from data.
Date Issued
2019-07-01
Date Acceptance
2019-06-03
Citation
SN Applied Sciences, 2019, 1 (7)
ISSN
2523-3963
Publisher
Springer Nature
Journal / Book Title
SN Applied Sciences
Volume
1
Issue
7
Copyright Statement
© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
License URL
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000475871000019&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
Reproducing Kernel Hilbert spaces
Linear discrete-time equations
Parameter estimation
Topological entropy
Linear control systems
Riccati equations
Identification
Control
METRIC-SPACES
IDENTIFICATION
Publication Status
Published
Article Number
UNSP 674
Date Publish Online
2019-06-07