Online Multiparameter Estimation of IPMSMs Considering Mutual Inductances and Rotor Position Compensation
Bibliographic record
Abstract
Accurate comprehensive parameter estimation and analysis are essential for high performance modeling and control strategy of interior permanent magnet synchronous motors (IPMSMs). This article proposes an improved electrical machine (EM) model considering cross coupling effects and rotor position compensation to accurately estimate parameters, including stator winding resistance, inductances and permanent magnet (PM) flux linkage. In this article, the stator winding resistance is estimated separately by decoupling from other unknown parameters through removing common elements, using basic measurements including speed, voltage and current. Furthermore, dq-axis inductances and mutual inductances are investigated and constructed into mapping over various currents through decoupling coefficients in the proposed mathematical model. Meanwhile, rotor position compensation is considered to reduce the effects of rotor position error on parameter estimation. The proposed approach can improve the accuracy of estimation which is validated by the experiment on a laboratory prototype IPMSM and compared with other estimation methods ignoring either mutual inductances or rotor position compensation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".