Correction of dq-Frame Deviation for Indirect Field-Oriented Control of Induction Motor
Bibliographic record
Abstract
This paper presents a method for estimating and compensating the transformation angle error in Indirect Rotor Flux-Oriented Control (IRFOC) of an induction machine (IM), caused by inaccuracies in electrical parameters. This method aims to improve the robustness of torque control in IM against variations in motor parameters. The proposed approach leverages an Extended Kalman Filter (EKF) to efficiently estimate the rotor flux. The estimated flux is then used to compute the flux position error from an analytical relation. This method operates on multiple reference frames, including αβ, dq , and a deviated rotating reference frame known as the d’q’ reference frame. The approach presented in this paper eliminates the need for online estimation of motor electrical parameters such as rotor resistance and mutual inductance. Simulation results are provided to demonstrate the effectiveness of the proposed approach.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".