Observer Assisted Current Reconstruction Method with Single DC-Link Current Sensor for Sensorless Control of Interior Permanent Magnet Synchronous Machines
Bibliographic record
Abstract
The paper presents an observer assisted current reconstruction based method to estimate the three phase currents by utilizing only one dc-link current sensor to realize position sensorless control of interior permanent magnet synchronous machine (IPMSM). The current reconstruction technique needs dc-link current information for at least two vector transition regions in a switching period to estimate the three phase currents. However, it is challenging at the sector boundary regions (immeasurable region) without shifting the space vectors of the pulse width modulation (PWM). In the proposed method, the current estimation is assured in the boundary region without shifting the PWM vectors with the help of assistive model-based observer. The assistive model-based observer is a Luenberger type observer that estimates the back electromotive force (EMF) based on the error between the reconstructed and estimated currents. An adaptive band-pass filter is applied to the estimated back EMF to eliminate the undesirable harmonics in the immeasurable region. The proposed scheme feedbacks the estimated current to the current controller and utilizes the estimated back EMF to estimate the position and speed. The algorithm is implemented in dSpace platform and validated by experiments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".