Torque and state estimation for real‐time implementation of multivariable control in sensorless induction motor drives
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Bibliographic record
Abstract
This study presents a strategy for estimating the states and the load torque to implement a feedback linearisation controller for induction motor drives. The multivariable control is carried out using input–output linearisation feedback law in order to track profiles of the rotational speed and the rotor flux amplitude. The unknown load torque is compensated by an estimator based on the speed error. The state estimation requires only the measurements of the stator voltages–currents. The estimation method is not invasive as no mechanical sensors are needed. Experimental platform equipped with sensors at the load side, for measuring the speed and the torque of the motor driven by the Opal‐RT real‐time system, was implemented to verify the accuracy of the proposed estimation method to implement the multivariable control.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it