Fuzzy-PI based model predictive control for speed control of BLDC motor

This paper proposes a new control algorithm based on Fuzzy-PI and Model predictive control for speed control of permanent magnet brushless DC motors. The proposed algorithm adopts the commonly used dual-loop control strategy with enhanced inner and outer loop controllers that help achieve an improve...

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Bibliographic Details
Published in2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) pp. 1 - 6
Main Authors Kumar, Basude Harish, Bhimasingu, Ravikumar, Kumar, V. Seshadri Sravan
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.12.2022
Subjects
BLDC motor
Brushless DC motors
fuzzy-PI
Heuristic algorithms
Hysteresis motors
Model Predictive Control
Permanent magnet motors
Prediction algorithms
speed control
Torque
Velocity control
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Summary:This paper proposes a new control algorithm based on Fuzzy-PI and Model predictive control for speed control of permanent magnet brushless DC motors. The proposed algorithm adopts the commonly used dual-loop control strategy with enhanced inner and outer loop controllers that help achieve an improved dynamic and steady-state response. The outer loop speed controller adopted in this work is a Fuzzy-PI controller, while the inner-loop current control is achieved using model predictive control. The proposed algorithm differs from the conventional algorithm, wherein the controller in the outer loop is typically a PI controller. Further, the inner-loop controller in most conventional methods is either a Hysteresis/PI controller based on Pulse width/pulse amplitude modulation. The conventional methods have the drawbacks of higher torque ripple and slower dynamic response compared to the proposed method. Hence, the fuzzy-PI and model predictive-based control is proposed to improve the dynamic response as well as the steady-state performance of the motor drive. The validation and performance analysis of the proposed approach is carried out using simulation studies performed in MATLAB/Simulink platform.
DOI:10.1109/PEDES56012.2022.10080614