Publication detail

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

FRIML, D. KOZUBÍK, M. VÁCLAVEK, P.

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

Total Least-Squares, Errors-in-Variables, Hierarchical Total Least-Squares, Nuisance Variables, PMSM Identification

Authors

FRIML, D.; KOZUBÍK, M.; VÁCLAVEK, P.

Released

13. 11. 2021

Publisher

IEEE

ISBN

978-1-6654-3554-3

Book

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

Pages from

1

Pages to

6

Pages count

6

URL

https://ieeexplore.ieee.org/document/9589402

Full text in the Digital Library

http://hdl.handle.net/11012/202278

BibTex

@inproceedings{BUT173146,
  author="Dominik {Friml} and Michal {Kozubík} and Pavel {Václavek}",
  title="On Improving TLS Identification Results Using Nuisance Variables with Application on PMSM",
  booktitle="IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society",
  year="2021",
  pages="1--6",
  publisher="IEEE",
  doi="10.1109/IECON48115.2021.9589402",
  isbn="978-1-6654-3554-3",
  url="https://ieeexplore.ieee.org/document/9589402"
}