Bibliographic record
Abstract
Abstract This paper introduces sensorless hysteresis direct torque control for permanent magnet synchronous motors to provide a reduced torque pulsation profile, which leads to a smaller mechanical vibration. The suggested algorithm utilizes the principle of vehicle steering, which continuously fixes the direction of the vehicle in the track. Moreover, the algorithm efficiently employs the output of torque and the output of flux error controllers used in basic hysteresis direct torque control to select two nonzero adjacent vectors. The initial switching time for the selected vectors is determined by a function that considers the absolute magnitude of the torque error and the magnitude of the flux error in addition to the space angle position of the stator flux. To reduce the processing time in the control signal flow, a suggested structure for switching the initial time of the selected vectors is developed. The final switching time of the vectors is adaptively adjusted according to the minimum required stator voltage to drive the load and according to the stored rotor energy that arises due to the inertia of the mechanical load connected to the motor shaft. The simulation results along with the experimental results show a fast dynamic response of torque, relatively reduced torque ripples, and reduced current harmonics compared to the basic hysteresis direct torque control.
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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) | 0.002 | 0.001 |
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".