Robust dq-Frame Alignment in Indirect RFOC of Induction Motors under Parameter Variations by means of a Super-Twisting Sliding Mode Observer
Bibliographic record
Abstract
This paper proposes a control strategy for robust estimation of the transformation angle error despite variations in motor parameters. The method focuses on identifying and compensating angle errors in Indirect Rotor Flux-Oriented Control (IRFOC). The main objective is to correct the misalignment between the (d, q) reference frame and the actual magnetic flux. In contrast to conventional methods that depend on adaptive techniques and real-time parameter estimation, the proposed strategy avoids the need of an online identification of motor parameters. To improve the accuracy of the transformation angle error estimation, the approach operates on multiple reference frames, including (α, β) frame, (d, q) frame, and a deviated rotating frame referred as the (d′, q′). It relies on a robust Sliding-Mode observer based on the Super-Twisting algorithm. Simulation results using the IRFOC scheme are provided to validate the performance of the proposed method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".