On Improving TLS Identification Results Using Nuisance Variables with Application on PMSM

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F21%3APU142091" target="_blank" >RIV/00216305:26620/21:PU142091 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9589402" target="_blank" >https://ieeexplore.ieee.org/document/9589402</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IECON48115.2021.9589402" target="_blank" >10.1109/IECON48115.2021.9589402</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On Improving TLS Identification Results Using Nuisance Variables with Application on PMSM

Popis výsledku v původním jazyce

This article presents a novel total least-squares based method for errors-in-variables model identification with a known structure. This method considers the errors of both input and output variables and thus achieves more accurate estimates compared to conventional ordinary least-squares based methods. The introduced method consists of two recursive total least-squares algorithms connected in a hierarchical structure, which allows for exploitation of nuisance variables and a priori known structure of the identified model. The total least-squares (TLS) method is introduced, and a new “nuisance improved hierarchical total least-squares” (nHTLS) method is derived. Its properties are discussed and proved by simulations. Furthermore, the method is applied in a practical experiment consisting of the state-space identification of the permanent magnet synchronous motor (PMSM). The introduced method is compared with TLS and proven to provide measurably superior dynamical behavior and smaller estimation error of results.

Název v anglickém jazyce

On Improving TLS Identification Results Using Nuisance Variables with Application on PMSM

Popis výsledku anglicky

This article presents a novel total least-squares based method for errors-in-variables model identification with a known structure. This method considers the errors of both input and output variables and thus achieves more accurate estimates compared to conventional ordinary least-squares based methods. The introduced method consists of two recursive total least-squares algorithms connected in a hierarchical structure, which allows for exploitation of nuisance variables and a priori known structure of the identified model. The total least-squares (TLS) method is introduced, and a new “nuisance improved hierarchical total least-squares” (nHTLS) method is derived. Its properties are discussed and proved by simulations. Furthermore, the method is applied in a practical experiment consisting of the state-space identification of the permanent magnet synchronous motor (PMSM). The introduced method is compared with TLS and proven to provide measurably superior dynamical behavior and smaller estimation error of results.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/TN01000024" target="_blank" >TN01000024: Národní centrum kompetence - Kybernetika a umělá inteligence</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society

ISBN

978-1-6654-3554-3

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

IEEE

Místo vydání

neuveden

Místo konání akce

Toronto

Datum konání akce

13. 10. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000767230601164