Research on the Control Algorithm for a Brushless DC Motor Based on an Adaptive Extended Kalman Filter
Tong Jinwu1, Zha Lifan2, Lu Xinyun2
1Engineering Training Center, School of Applied Technology, Nanjing Institute of Technology, Nanjing 211167, China.
Sensors (Basel, Switzerland)
|February 13, 2026
Summary
This study introduces an Adaptive Extended Kalman Filter (AEKF) to improve state estimation for sensorless Brushless DC (BLDC) motors. The AEKF enhances accuracy and robustness during dynamic conditions, outperforming the traditional Extended Kalman Filter (EKF).
Area of Science:
- Electrical Engineering
- Control Systems
- Robotics
Background:
- Traditional Extended Kalman Filter (EKF) struggles with performance degradation in sensorless Brushless DC (BLDC) motor control due to model mismatches during dynamic operations.
- Sudden speed and load changes in BLDC motors necessitate improved state estimation for accurate control.
- Existing methods lack adaptability to system uncertainties and external disturbances in sensorless BLDC applications.
Purpose of the Study:
- To propose an Adaptive Extended Kalman Filter (AEKF) algorithm to overcome the limitations of the traditional EKF in sensorless BLDC motor control.
- To enhance the state estimation accuracy and robustness of rotor position and speed under dynamic operating conditions.
- To provide an effective solution for high-dynamic performance sensorless control of BLDC motors.
Main Methods:
- Developed an Adaptive Extended Kalman Filter (AEKF) algorithm incorporating a robust weighting strategy based on Mahalanobis distance.
- Implemented a dynamically adjusted adaptive forgetting factor for online updating of the innovation covariance.
- Simulated the AEKF algorithm under various dynamic conditions including low-speed start-up, speed step changes, and sudden load applications.
Main Results:
- The AEKF algorithm significantly improved the estimation accuracy of rotor position and speed compared to the traditional EKF.
- AEKF demonstrated accelerated dynamic response, reduced overshoot, and enhanced disturbance rejection robustness.
- Improved adaptability to system uncertainties and external disturbances was observed in the AEKF.
Conclusions:
- The proposed AEKF algorithm offers a superior state estimation solution for sensorless BLDC motor control under dynamic conditions.
- AEKF effectively addresses model mismatches and enhances overall system performance and reliability.
- This research provides a valuable tool for achieving high-dynamic performance in sensorless BLDC motor applications.
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