Model-Free Predictive Current Control for Multilevel Voltage Source Inverters
Bibliographic record
Abstract
This article proposes a generalization of the model-free predictive current controller (MFPCC) for multilevel voltage source inverters (VSIs). MFPCC is an alternative to mitigate the parameter sensitivity faced by model-based PCC (MPCC), but it requires a constant update of the stored current variations (CVs) associated with each VSI state to provide a prediction, which results in a satisfactory closed-loop response. When a multilevel VSI is considered the number of states increases, which results in a decrease of the CV update rate, since only one state can be updated at each sample. To solve this issue, the extended adjacent state scheme is used to reduce the possible number of solution candidates and then a controlled CV is used to compensate the updated CV. Simulation and experimental results obtained with a sampling rate of 5 kHz show that the proposed MFPCC exhibits performance similar to the one of MPCC for nominal parameters. However, a better current response is obtained in the case of the load parameter mismatch, especially at steady state.
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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) | 0.000 | 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 | 0.001 |
| 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".