Comparison of different approaches to continuous-time system identification from sampled data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F18%3APU129119" target="_blank" >RIV/00216305:26620/18:PU129119 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/EECS.2017.21" target="_blank" >http://dx.doi.org/10.1109/EECS.2017.21</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EECS.2017.21" target="_blank" >10.1109/EECS.2017.21</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of different approaches to continuous-time system identification from sampled data

Popis výsledku v původním jazyce

This article deals with different approaches to continuous-time system identification from sampled data. Continuous-time system identification is important problem in control theory. Continuous time models provide many advantages against discrete time models because of better physical insight into the system properties. The traditional approach with least squares method with state variable filters is presented. Two alternative approaches to continuous-time identification are proposed. The generalized Laguerre functions method and the method based on least squares estimation with numerical solution of differential equation are introduced. These three different approaches to continuous-time system identification from sampled data are compared on the example. It is shown that proposed alternative methods can give better results in terms of relative root mean square error of the outputs of the identified systems than the least squares method with state variable filters.

Název v anglickém jazyce

Comparison of different approaches to continuous-time system identification from sampled data

Popis výsledku anglicky

This article deals with different approaches to continuous-time system identification from sampled data. Continuous-time system identification is important problem in control theory. Continuous time models provide many advantages against discrete time models because of better physical insight into the system properties. The traditional approach with least squares method with state variable filters is presented. Two alternative approaches to continuous-time identification are proposed. The generalized Laguerre functions method and the method based on least squares estimation with numerical solution of differential equation are introduced. These three different approaches to continuous-time system identification from sampled data are compared on the example. It is shown that proposed alternative methods can give better results in terms of relative root mean square error of the outputs of the identified systems than the least squares method with state variable filters.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2017 European Conference on Electrical Engineering and Computer Science (EECS) (2017)

ISBN

978-1-5386-2085-4

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

61-65

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Bern

Datum konání akce

17. 11. 2017

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000455867600013