Detail publikačního výsledku

Online recursive least square parameter estimation for PMS machine

KEČKÉŠ, A.

Originální název

Online recursive least square parameter estimation for PMS machine

Anglický název

Online recursive least square parameter estimation for PMS machine

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

This paper presents the simulation results of the Recursive Least Squares (RLS) algorithm for estimating key motor parameters, including stator resistance and inductances. The algorithm is implemented and tested in MATLAB Simulink to evaluate its accuracy and adaptability under varying operating conditions. The results demonstrate the effectiveness of RLS in real-time parameter estimation, highlighting its potential for improving motor control precision and system stability.

Anglický abstrakt

This paper presents the simulation results of the Recursive Least Squares (RLS) algorithm for estimating key motor parameters, including stator resistance and inductances. The algorithm is implemented and tested in MATLAB Simulink to evaluate its accuracy and adaptability under varying operating conditions. The results demonstrate the effectiveness of RLS in real-time parameter estimation, highlighting its potential for improving motor control precision and system stability.

Klíčová slova

Motor control , parameter estimation , recursive least squares , stator resistance , inductance estimation , real-time estimation

Klíčová slova v angličtině

Motor control , parameter estimation , recursive least squares , stator resistance , inductance estimation , real-time estimation

Autoři

KEČKÉŠ, A.

Rok RIV

2026

Vydáno

30.07.2025

ISBN

978-80-214-6321-9

Kniha

Proceedings I of the 31st Conference STUDENT EEICT 2025: General papers

Strany počet

3

BibTex

@inproceedings{BUT201543,
  author="Adam {Kečkéš}",
  title="Online recursive least square parameter estimation for PMS machine",
  booktitle="Proceedings I of the 31st Conference STUDENT EEICT 2025: General papers",
  year="2025",
  pages="3",
  isbn="978-80-214-6321-9"
}