Correction of Current Measurement Scaling and Offset Errors for Permanent Magnet Synchronous Machine Drives
Bibliographic record
Abstract
Current measurement errors in permanent magnet synchronous motor (PMSM) drives can lead to undesired torque and speed ripples, significantly impairing control performance. To address this issue, this paper presents a novel online current error correction method for PMSM drives. The proposed approach takes into account both current scaling errors and offset errors, aiming to achieve precise error correction without relying on signal injection or prior knowledge of machine parameters. First, the relation between the different types of current errors and the machine speed harmonic is derived, laying the foundation for current measurement correction. Then, the speed harmonic is explored to search for actual current errors with the gradient descent algorithm. By minimizing the specific order of speed harmonics, the proposed algorithm achieves precise correction for current measurement errors without signal injection or knowledge of machine parameters. Through extensive simulation studies, the efficacy of the proposed current error correction algorithm is thoroughly validated. The results demonstrate significant improvements in control performance, confirming the method's ability to mitigate both current offset and scaling errors in a PMSM drive.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".