Discrete Terminal Super-Twisting Current Control of a Six-Phase Induction Motor
Bibliographic record
Abstract
In this manuscript, the high-accuracy stator currents tracking issue is considered for a six-phase induction motor subject to external perturbations and uncertainties due to unmeasurable rotor currents and electrical parameter variations. To achieve the control goals, the common two-cascade controllers structure is required for this type of motor. The first controller in the outer loop consists of a proportional integral to regulate the speed. Then, the second is the proposed inner nonlinear stator currents controller based on a robust discrete-time terminal super-twisting algorithm supported by the time-delay estimation method. For the design procedure, the discrete-time stator currents dynamics are derived; for example, the vector of the matched perturbations and unmeasurable rotor currents are specified to simplify the estimation. A detailed stability analysis of the closed-loop error dynamics using Lyapunov theory is given. Finally, a real asymmetrical six-phase induction motor is used to implement in real-time the developed method and to illustrate its effectiveness and robustness. The results obtained reveal a satisfactory stator currents tracking in steady state and transient conditions and under variation in the magnetizing inductance. Moreover, a comparative study with an existing method in steady state for two different rotor speeds is presented to show the superiority of the proposed discrete-time technique.
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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".