Extended Operation of Brushless DC Motors Beyond 120° Under Torque Ripple Reduction Control
Bibliographic record
Abstract
Hall-sensor-based brushless dc (BLDC) motors are extensively utilized in many applications due to their simple manufacturing and straightforward control. The conventional commutation method uses the 120° commutation logic, which can be directly derived from the Hall signals. The advantage of this method is that it naturally approximates the maximum torque per Ampere (MTPA) operation, but the high torque ripple and low dc voltage utilization are undesirable features. Many algorithms have been proposed in the literature to minimize the torque ripple during the conduction and commutation intervals. This paper proposes a new torque control strategy that can continuously extend the operation from 120° to 180° and maintain the torque ripple reduction during both conduction and commutation intervals by dynamically adjusting the duty cycle based on the reference torque, the estimated torque, and the derivative of torque. The proposed method reduces the torque ripple during the commutation interval, even in high-speed operation, by increasing the conduction angle. The proposed method is analyzed under different speed and back EMF shapes, and its effectiveness is demonstrated experimentally on a typical industrial BLDC motor.
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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 | 0.000 | 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".