Simultaneous Estimation of Speed and Rotor Resistance in Sensorless Induction Motor Vector Controlled Drive
Bibliographic record
Abstract
In this paper a new sensorless indirect vector controlled induction motor drive robust against rotor resistance variation is presented. The speed and rotor resistance are estimated simultaneously, which is reported in many papers as impossible. The estimation is achieved using a reduced order Kalman filter to reduce the computational burden. This algorithm uses a reduced order model of the motor. The model takes into account the coupling between the electrical and mechanical modes, which is true for small size machines. The method proposed in this paper is applicable to a large category of induction motor drives with a gradually varying load torque such as viscous friction, fan/blower and centrifugal pump. A fully real-time digital simulation, a new powerful tool for rapid control prototyping, is carried out to verify the effectiveness of the proposed method. Results show that accurate estimation is achieved under both transient and steady state conditions without injecting any external signal. This achievement is, to the best of authors' knowledge, reported for the first time and is believed to be of great importance for induction machine sensorless control.
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 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 | 0.000 | 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 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".