Real-Time High Performance of Induction Motor Drive Using Hybrid Fuzzy-Sliding Mode Controllers
Bibliographic record
Abstract
Obtaining the required performance, stability, and robustness in real-time control of induction motors usually requires the use of complex controllers, however through multiple experimentations, many challenges have arisen from such methods. The complex structure of control methods in real-time applications is usually computationally challenging and energy consuming, hence the need for a simple control strategy to overcome these challenges, in this paper, we focus on designing an advanced hybrid control strategy with a simple design applied to an induction motor. Mainly, the hybrid controller used in this study has the benefits of joining the best performance of both fuzzy logic controller and sliding mode controller, specifically designed to handle each phase separately, the transition phase and the steady phase. A fuzzy controller intervenes as a supervisor in our control structure, more specifically it manages the switch from one type of control to the other taking into account the intervention phase of each type of controller by commanding the rate of both controllers. Control performance analysis was carried out in a real experimental setup to validate the efficiency and robustness of the proposed hybrid controller and confirm its effectiveness in handling the compromise between overshoot and response time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".