Design of a New Direct Torque Control Using Synergetic Theory for Double Star Induction Motor
Bibliographic record
Abstract
This paper describes a new Direct Torque Control (DTC) scheme to the synthesis of controllers based on the theory of synergetic control (SC) for two level inverter fed double star induction motor (DSIM) drive. The controllers’ synthesis method is totally analytical, and is based on non-linear models of the DSIM. The proposed synergetic control scheme requires creation of the space attractors and artificial manifolds that reflect the desirable operating modes of the DSIM. The combination between the DTC and the SC laws provides asymptotic stability with respect to the required operating regimes, reduces the THD of stator currents, invariance to external disturbances, and robustness to variation of DSIM parameters. The performance of the proposed approach has been tested under different operating conditions. With respect to their dynamic characteristics, synergetic controllers (SCs) are superior to the existing types of PI, Sliding Mode (SM) and Fuzzy Logic (FL) controllers.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".