Efficient Maximum Torque Per Ampere (MTPA) Control of Interior PMSM Using Sparse Bayesian Based Offline Data-Driven Model With Online Magnet Temperature Compensation
Bibliographic record
Abstract
The maximum torque per ampere (MTPA) is popular control strategy for interior permanent synchronous machines (PMSMs) and MTPA point is dependent on the magnetic saturation and magnet temperature. This article proposes a novel MTPA control method combining offline model and online compensation model for interior PMSM control. In the proposed approach, an offline sparse Bayesian based data driven model is derived from the machine equations to consider magnetic saturation, and an online compensation model is proposed to compensate the magnet temperature. The MTPA point can be derived by combining both the offline and online models, in which both saturation and temperature effects are considered to ensure the performance of MTPA point tracking. Compared with the offline methods, the proposed approach employs the sparse vector to represent the MTPA model with less computation and memory consumption and considers the temperature effect with better robustness. Compared with the online methods, the proposed approach only compensates the offline model with online temperature effect, which is less sensitive to noise and uncertainties and involves less computation. The proposed approach is validated with comparisons and experiments on a laboratory interior PMSM drives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".