# Research on the Control Algorithm for a Brushless DC Motor Based on an Adaptive Extended Kalman Filter

## TL;DR

A new adaptive filter improves control of brushless DC motors by better handling system uncertainties and disturbances.

## Contribution

The AEKF algorithm introduces a robust weighting strategy and dynamic forgetting factor for improved state estimation in BLDC motors.

## Key findings

- The AEKF algorithm shows superior estimation accuracy for rotor position and speed compared to traditional EKF.
- It improves dynamic response and robustness under low-speed startup and sudden load changes.
- The algorithm enhances disturbance rejection and reduces overshoot in BLDC motor control.

## Abstract

What are the main findings?
An Adaptive Extended Kalman Filter (AEKF) algorithm is proposed, featuring a robust weighting strategy and a dynamic forgetting factor to enable online innovation covariance updating.The designed AEKF demonstrates superior estimation accuracy, faster dynamic response, and enhanced robustness compared to the traditional EKF under various dynamic operating conditions.

An Adaptive Extended Kalman Filter (AEKF) algorithm is proposed, featuring a robust weighting strategy and a dynamic forgetting factor to enable online innovation covariance updating.

The designed AEKF demonstrates superior estimation accuracy, faster dynamic response, and enhanced robustness compared to the traditional EKF under various dynamic operating conditions.

What are the implications of the main findings?
This work provides an effective and adaptive state estimation solution for high-performance sensorless control of BLDCs, directly addressing challenges from model uncertainties and external disturbances.The algorithm framework offers a valuable reference for the state observation of nonlinear electromechanical systems, contributing to the advancement of robust and intelligent control strategies in related fields.

This work provides an effective and adaptive state estimation solution for high-performance sensorless control of BLDCs, directly addressing challenges from model uncertainties and external disturbances.

The algorithm framework offers a valuable reference for the state observation of nonlinear electromechanical systems, contributing to the advancement of robust and intelligent control strategies in related fields.

To address the performance degradation of the traditional Extended Kalman Filter (EKF) in state estimation for sensorless brushless DC motor (BLDC) control under dynamic operating conditions, such as sudden speed and load changes—a degradation caused primarily by model mismatches—this paper proposes an Adaptive Extended Kalman Filter (AEKF) algorithm. The proposed algorithm incorporates a robust weighting strategy based on the Mahalanobis distance and a dynamically adjusted adaptive forgetting factor. This integration establishes an estimation mechanism capable of online updating of the innovation covariance, thereby enhancing the state observer’s adaptability to system uncertainties and external disturbances. Simulation results demonstrate that, compared to the traditional EKF, the designed AEKF algorithm significantly improves the estimation accuracy of rotor position and speed under various operating conditions, including low-speed start-up, speed step changes, and sudden load applications. Furthermore, it accelerates dynamic response, suppresses overshoot, and enhances the system’s disturbance rejection robustness. This work provides an effective state estimation solution for high-dynamic performance sensorless control of BLDC.