PMSM Combination Modeling for Multiparameter Estimation Using Bayesian Learning With Inverter Distortion Cancellation and Temperature Compensation
Bibliographic record
Abstract
Permanent magnet synchronous machine (PMSM) drives with better efficiency are highly demanded, and accurate flux linkage and inductance models or maps are critical to achieve such drives. However, precise modeling and estimation of these parameters should employ redundant data and are affected by magnetic saturation and inverter distortion. This paper firstly derives a flux linkage combination model from machine model for flux linkage and inductance estimation, in which inverter distortion is cancelled and thus inverter influence is minimized for performance improvement. To consider magnetic saturation, radial basis functions are employed to model the nonlinear flux linkages with a small number of relevance vectors, which can effectively depict the nonlinear variation. Bayesian learning approach is then explored to estimate the sparse coefficients of the flux linkage model in the context of radial basis functions, which can deal with non-Gaussian noise to improve the estimation accuracy and guarantee flux linkage model with better computation efficiency and less memory occupation. Moreover, temperature effect is considered to ensure the model accuracy under temperature rise. The proposed approach is validated with experiments and comparisons on a laboratory 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".