Model Predictive-Position Sensorless Control of PMSM with Non-Sinusoidal Back-EMF
Bibliographic record
Abstract
Model predictive control (MPC) has proven to be an efficient control technique for torque ripple minimization in permanent magnet synchronous motors (PMSMs). The torque ripple in practical PMSM is primarily caused by the non-sinusoidal back-EMF owing to the exact shape and structure of the rotor magnets and the equivalent non-sinusoidal flux distribution within the air gap. The predictive control techniques use the rotor flux lookup table (LUT) to estimate the motor output torque under non-sinusoidal back-EMF. This work proposes a rotor position estimation technique by using the rotor flux LUT and by reverse calculating the α and β components of the rotor flux components from the measured motor phase currents and inverter control signals. The proposed technique estimates the rotor angle irrespective of the initial position without requiring additional sensors. The sensorless control is modeled and tested in the simulation environment for a PMSM of 7 kW rated power, and the design is validated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".