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. Accurate data-driven models derived from these techniques are essential for designing efficient feedback control systems, enabling precise motor dynamics representation and facilitating early fault detection by identifying deviations from normal operation. A detailed simulation of the BLDC motor under no-load conditions is performed to analyze the speed response and ripple effects in torque and speed, underscoring the need for effective modeling and control. Comprehensive datasets are generated to develop LS an

SUBMITTER: Khan MA 

PROVIDER: S-EPMC11904227 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature