Optimized System Identification (SI) of Brushless DC (BLDC) motor using Data-Driven Modeling Methods

Abstract This study investigates the nonlinear dynamics of Brushless DC (BLDC) motors using the MATLAB/Simulink platform, emphasizing system identification through the Least Squares (LS) method and Nonlinear Autoregressive Network With Exogenous Inputs (NARX) models with variable regressors. Accurat...

Полное описание

Библиографические подробности

Главные авторы:	Muhammad Aseer Khan, Dur-e-Zehra Baig, Husan Ali, Fahad R. Albogamy
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Nature Portfolio 2025-03-01
Серии:	Scientific Reports
Предметы:	Brushless direct current (BLDC) motor Data-driven modeling Electric vehicles (EVs) Modeling and simulation approaches Nonlinear Autoregressive Network With Exogenous Inputs (NARX) System identification (SI)
Online-ссылка:	https://doi.org/10.1038/s41598-025-93444-0
Метки:	Добавить метку Нет меток, Требуется 1-ая метка записи!