A novel adaptive observer for very fast estimation of stator resistance in sensorless induction motor drives
Bibliographic record
Abstract
The performance of sensorless vector-controlled induction motor drives is generally poor at very low speeds due to offset and drift components in the acquired feedback signals, and the increased sensitivity to model parameter mismatch. The stator resistance variations resulting from temperature and frequency changes, produces deviations in the flux, and as a result the speed estimates particularly at very low speeds are greatly affected. Therefore a compensation scheme for the parameter variations is vital, especially in very low speed applications of sensorless induction motor drives. This paper presents a novel method of estimating the stator resistance of an induction motor, based on adaptive control theory. In this novel scheme, an adaptive pseudoreduced-order flux observer (APFO) is used instead of the adaptive full-order flux observer (AFFO). In comparison with the AFFO, this method consumes less computational time, and provides a better performance at very low speeds. Both simulation and experimental results of the proposed stator resistance scheme have shown that the proposed method is faster than those based on AFFO, and further the simulation results have demonstrated satisfactory performance over an entire range of resistance variations from 0 to 100%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".