Magnetic-Characteristic-Free High-Speed Position-Sensorless Control of Switched Reluctance Motor Drives With Quadrature Flux Estimators
Bibliographic record
Abstract
High-speed position-sensorless control of switched reluctance motor (SRM) drives is often realized by searchinga prioriflux linkage lookup table. However, obtaining the nonlinear flux linkage characteristic requires time-consuming offline measurement and occupies large memory storage. To deal with this problem, this article proposes a new position estimation scheme for SRM drives without using any magnetic characteristics. This feature is achieved by using a quadrature flux estimator (QFE) that can extract the fundamental flux and its quadrature signal from the real-time calculated flux linkage. The flux bias and flux harmonics can also be suppressed by the QFE’s adaptive bandpass capability. Therefore, the proposed method can reduce the nonlinearity in SRM flux linkage and derive simple sine–cos position signals. Afterward, the rotor position and speed can be estimated from the orthogonal flux signals using a three-phase phase-locked loop. To baseline the advantages, comparative experimental validation with the conventional method is conducted on a three-phase 12/8 SRM test bench. The results show that the proposed scheme can achieve the same estimation accuracy as the conventional method even though no magnetic characteristics are used.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".