Multi‐rate real‐time model‐based parameter estimation and state identification for induction motors
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".