Black-box Identification and Validation of an Induction Motor in an Experimental Application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents a simple approach to identify an induction motor model which is used in a traction control system for the purpose of slip prevention. In this study, an identified model has been extracted by real and identified parameters of the induction motor in the MATLAB, Simulink; as the purpose of this study is to identify a linear model for the induction motor, the speed control loops have been opened, therefore d-q voltages have been designed as the exiting inputs. After several considerations and examination, ARMAX model has been selected as a mathematical model in the identification process, then the open loop identified model has been replaced in the closed-loop structure, and by using the real data measurements, obtained from the closed-loop system in the Advanced Motion Control Laboratory, the open loop extracted model is verified.
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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 | 0.001 |
| 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 it