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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".