Fast Maximum Torque per Ampere (MTPA) Angle Detection for Interior PMSMs using Online Polynomial Curve Fitting
Bibliographic record
Abstract
For interior permanent magnet synchronous machine (IPMSMs), maximum torque per ampere (MTPA) control aims to find the MTPA angle to maximize the control objective (the ratio of output torque to stator current). This paper proposes a novel online polynomial curve fitting technique for fast and accurate MTPA angle detection, which is motivated by the fact that the objective increases before MTPA angle and decreases after MTPA angle. This paper proposes a polynomial based objective model and identifies the polynomial parameters from a few test data for direct MTPA angle calculation. The proposed approach can avoid the time-consuming search process resulting in fast detection speed in comparison to existing search-based methods. In implementation, the current angle is set to a few test values to obtain the data for online curve fitting and MTPA angle calculation, in which there is no need of machine inductances and PM flux linkage. Moreover, the proposed polynomial model is analyzed to obtain the number of test data required for fast and accurate MTPA angle detection. The proposed approach is validated with extensive experiments and comparisons with existing methods on a laboratory IPMSM.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".