Model Predictive Current Control of Two-Level Four-Leg Inverters—Part II: Experimental Implementation and Validation
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Bibliographic record
Abstract
This paper describes the experimental implementation of the model predictive current control algorithm for a two-level four-leg inverter operating under balanced, unbalanced, and nonlinear loading conditions. The proposed scheme is designed to predict the future behavior of the load currents for each of the 16 possible switching states of the converter. The control method chooses a switching state that minimizes the error between the output currents and their references. The algorithm is embedded using a MATLAB/Simulink software environment, and experimental results based on the dSPACE DS1103 controller are provided. These results verify the advantages of the proposed control strategy in terms of robustness under filter and load parameter variations, average neutral-leg switching frequency reduction, reference tracking error, and percentage total harmonic distortion.
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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 it