Current Tracking Adaptive Control of Brushless DC Motors

ABSTRACT In this article, the practical problem of current tracking for Brushless Direct Current motors is approached. The main feature of the contribution lies in the fact that uncertainty in the parameters of the motor's model is considered. This lack of knowledge is compensated by proposing an adaptive control scheme that directly deals with the uncertainty in the electrical parameters without requiring any knowledge, neither of the mechanical parameters nor the load torque, as long as this external disturbance remains bounded. The strength of the proposed control scheme appears from the passivity‐based decomposition of the motor's model allowing robust control of the electrical subsystem without requiring mechanical information, providing the rotor speed remains bounded. Moreover, achievement of the control objective does not rely on estimated parameter convergence to the real values. The stability properties of the proposed scheme are mathematically proved using classical results from the passivity and the adaptive control theories, which makes it attractive from a practical perspective. Moreover, it exhibits a simple structure and is easy to tune. The usefulness of the contribution is validated in an experimental setting using the BLY344S‐240V‐3000 motor manufactured by Anaheim Automation.

Authors

Institutions

Publication Details

Journal
International Journal of Adaptive Control and Signal Processing
Published
2026-01-05
DOI
https://doi.org/10.1002/acs.70032
Primary Topic
Control and Stability of Dynamical Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Current Tracking Adaptive Control of Brushless DC Motors

Fernanda Ramos‐García, Gerardo Espinosa‐Pérez, Axel Coronado‐Andrade, Luis Alvarez‐Icaza
International Journal of Adaptive Control and Signal Processing
Control and Stability of Dynamical Systems
article

Current Tracking Adaptive Control of Brushless DC Motors

Fernanda Ramos‐García, Gerardo Espinosa‐Pérez, Axel Coronado‐Andrade, Luis Alvarez‐Icaza
article en

Abstract

ABSTRACT In this article, the practical problem of current tracking for Brushless Direct Current motors is approached. The main feature of the contribution lies in the fact that uncertainty in the parameters of the motor's model is considered. This lack of knowledge is compensated by proposing an adaptive control scheme that directly deals with the uncertainty in the electrical parameters without requiring any knowledge, neither of the mechanical parameters nor the load torque, as long as this external disturbance remains bounded. The strength of the proposed control scheme appears from the passivity‐based decomposition of the motor's model allowing robust control of the electrical subsystem without requiring mechanical information, providing the rotor speed remains bounded. Moreover, achievement of the control objective does not rely on estimated parameter convergence to the real values. The stability properties of the proposed scheme are mathematically proved using classical results from the passivity and the adaptive control theories, which makes it attractive from a practical perspective. Moreover, it exhibits a simple structure and is easy to tune. The usefulness of the contribution is validated in an experimental setting using the BLY344S‐240V‐3000 motor manufactured by Anaheim Automation.

International Journal of Adaptive Control and Signal Processing
Universidad Nacional Autónoma de México (MX)
Affordable and clean energy
Openalex Percentile: Top 95%
Control and Stability of Dynamical Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.