Multi‐rate real‐time model‐based parameter estimation and state identification for induction motors
Why this work is in the frame
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Bibliographic record
Abstract
This study presents multi‐rate parameter and state estimation methods for the induction motor. Based on multi‐rate control theory and the extended Kalman filter (EKF) theory, a multi‐rate EKF algorithm including input and output algorithms is proposed for load torque estimation in the induction motor. The methods are implemented in real‐time on PC‐cluster node which acts as the controller for an induction motor experimental set‐up. Rotor time constant is a sensitive variable in indirect field‐oriented control method. A multi‐rate model reference adaptive system (MRAS) is proposed to estimate the rotor time constant in order to guarantee the high‐performance control of induction motor. Experimental result verified the effectiveness of the algorithms. Simulations compare the multi‐rate EKF algorithm with the traditional single‐rate EKF algorithm performance to show improved performance of load torque estimator. The comparison between the traditional MRAS and the multi‐rate MRAS shows the superiority of the proposed method, with a satisfactory accuracy.
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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 | 0.001 |
| 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