Experimental Assessment of Sliding Mode Current Control with Exponential Reaching Law for an Induction Machine Drive Fed by a Matrix Converter
Bibliographic record
Abstract
Matrix Converters (MCs) are considered an exciting option in electrical motor drives for applications where size and weight are critical, for example aerospace or automotive. Several control techniques have been proposed to exploit the MC’s benefits and get the desired performance. Among them, Sliding Mode Control (SMC) is quite attractive due to its robustness and fast response. However, Chattering can appear in the SMC strategy. Consequently, the Exponential Reaching Law (ERL) is employed to solve this issue in this paper. The proposed control structure includes a modulation stage based on the space vector modulation technique and a Kalman filter-based rotor current estimator. Experimental results are provided to validate the proposed method using a custom test bench based on SiC-MOSFETs MC and a three-phase induction machine. A comparison between the proposed SMC-ERL and classic SMC is also provided to highlight the improvements obtained.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".