Finite Control Set Model Predictive Control for AC–DC Matrix Converter With Virtual Space Vectors
Bibliographic record
Abstract
AC–DC matrix converter is a kind of bidirectional converter features buck-type rectifying and boost-type inverting. Considering the coupling of ac current and dc current, and the requirement on safe commutation, the finite control set model predictive control (FCS-MPC), featuring multiple-objective capability and direct generation of gating signals without pulse width modulation (PWM) scheme, is very suitable for this topology. Also, fast transient can be obtained with the FCS-MPC. However, in conventional FCS-MPC, only one space vector in each sampling period is applied. The performance will be degraded due to the low switching frequency under limited sampling frequency in practice. In this article, an FCS-MPC with virtual space vectors is proposed for a bidirectional ac–dc matrix converter. This article uses several virtual space vectors, each formed by two real space vectors, to improve the control performance without increasing sampling frequency. To further reduce the computation burden, an efficient method to preselect space vectors is proposed. Furthermore, a thorough analysis of the effect of parameter mismatch in the proposed FCS-MPC is conducted to ensure robust performance. Both simulation and experimental results are presented to verify the effectiveness of the proposed control strategy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".