Study on QEMF Model and Adaptive Full-Order Observer Design for Universal Sensorless Control of IPMSMs
Bibliographic record
Abstract
A quadratic extended electromotive force (QEMF) model enabled the use of traditional high-speed adaptive estimation methods combined to high-frequency signal injection (HFSI) for full-range sensorless position control of interior permanent magnet synchronous motors (IPMSMs). However, the first QEMF model presented in the literature only works with HFSI in the$q$-axis, due to the QEMF being a function of the$q$-axis current derivative. The$q$-axis HFSI is known to produce undesired torque ripple. Furthermore, the$q$-axis signal injection can be insufficient for low-speed position estimation in IPMSMs with low salience. A recent study demonstrated that the QEMF concept can be modeled as a function of the$d$-axis current derivative. In this article, the influence of the$d$-axis HFSI on QEMF is investigated and compared with the$q$-axis HFSI method. Furthermore, the electromotive force-based observers are usually designed for medium- to high-speed operation. Here, the adaptive full-order observer is adapted in order to achieve universal sensorless control through the QEMF-based$d$-axis HFSI. The state observer and adaptive law are designed by a cascade methodology, which guarantees accurate extended electromotive force (EEMF) estimation and robustness throughout the entire operating speed range. Experimental results are presented in order to validate the proposed method and analysis under full-range sensorless control.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".