Robust Incremental Bayesian Learning Based Online Flux Linkage Estimation for PMSM Drives
Bibliographic record
Abstract
For the permanent magnet synchronous machine (PMSM), parameter estimation can be greatly affected by the measurement uncertainty, but few efforts are made to reduce the uncertainty level for estimation performance improvement. Therefore, this article proposes an efficient and robust incremental Bayesian learning approach for PMSM parameter estimation. The measurement uncertainty is evaluated to guide the selection of informative measurements, and the estimation uncertainty is provided to indicate the confidence in using the estimated results. Specifically, a Bayesian learning strategy with a layered noise model is proposed for nonlinear flux linkage estimation. The measurement uncertainty level is estimated from the proposed Bayesian learning model, which is utilized to adaptively select the most informative data and delete the noninformative data for parameter estimation. This contributes to improving estimation accuracy and computation efficiency. Moreover, the estimation uncertainty is also determined by the proposed model, which can be used to indicate if the estimated results can be trusted and utilized in practical applications. The proposed approach is evaluated on a laboratory interior PMSM under various operating conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".