Improvement of Direct Torque Control Performances for Induction Machine Using a Robust Backstepping Controller and a New Stator Resistance Compensator
Bibliographic record
Abstract
This paper aims to propose an improved Direct Torque Control (DTC) strategy with Space Vector Modulation (SVM) for induction machines (IM). The performance enhancement is operated by using the nonlinear backstepping strategy. This approach is proposed to ensure a robust control against different uncertainties and external disturbances and to reduce torque and flux ripples. The backstepping controller uses the stator resistance of the machine for estimation of the stator flux. The variations of the stator resistance due to the changes in temperature or frequency make the operation of this control difficult at low speeds. A new method for the estimation of stator resistance changes during machine operation is proposed. It is based on a Super Twisting strategy. The design of the proposed DTC strategy law is developed theoretically and realized through numerical simulations. Different operating conditions are applied to check the ability and robustness of the proposed control strategy, such as steady state, speed reversal maneuver, low-speed operation, parameters variation and load application. Results clearly show improvement in DTC at low speeds.
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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 | 0.001 |
| 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) | 0.001 | 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".