Nonlinearity Estimation and Compensation for Accurate PMSM Modeling and Voltage Prediction
Bibliographic record
Abstract
For permanent magnet synchronous machine (PMSM), the machine model is critical to predict the operating states for motor control, which, however, can be greatly affected by system nonlinearities. Hence, this paper investigates accurate machine modeling for control and parameter estimation. In the proposed approach, the PMSM model with saturated inductances is used as the base model, and this paper investigates modeling and compensation of the offsets to the base model due to system nonlinearities such as saturation and core loss effects for accurate machine modeling and voltage prediction. Specifically, the offsets to the base model are modeled using nonlinear functions with variable coefficients to compensate saturation and core loss effect, which can achieve better accuracy without changing the model structure. A differential estimation model is derived to estimate the model coefficients from a small amount of measurements with simplified procedure. Moreover, the model offset calculation is both computation and memory efficient with simplified implementation. The contribution is to improve the machine model accuracy and achieve precise voltage prediction for practical applications. Experiments, comparisons and the application to temperature estimation are conducted on a test interior PMSM to validate the proposed approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".