Torque Ripple Minimization Technique of Position Sensorless BLDC Motor for Variable Speed Drives
Bibliographic record
Abstract
Brushless Direct Current (BLDC) motors are advantageous because of their higher efficiency, higher speed operations and higher power density. Industrial applications demand BLDC motors free from torque ripple. The torque ripple is due to the unequal commutation period between the energised phase and unenergized phase current. It is a perilous problem in sensorless BLDC drive as it leads to speed oscillations, acoustic noise, serious faults, and vibration in machines. The torque ripple can be reduced either by improving motor design parameter or by improving the motor control strategy. This paper proposes a Proportional Integral (PI) controller-based control scheme for a cuk converter driven sensorless BLDC motor to reduce the torque ripple. The proposed scheme invokes Zero Crossing Point (ZCP) detection with back emf sensing approach. The presence of inductor reduces the ripple in the input and output currents. The performance of the strategy is verified using MATLAB R2018a Simulink for different operating conditions of a BLDC drive and the results prove that the recommended scheme decreases the torque ripple compared to the conventional scheme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".