Systematic Finite-Control-Set Model Predictive Control Design with Unified Model for Isomorphic and Dual Power Converters
Bibliographic record
Abstract
Finite-control-set model predictive control (FCS-MPC) has been applied to various power converters successfully in the last decades. However, the FCS-MPC algorithm for a power converter is usually a case-by-case design process. In this article, instead of designing an FCS-MPC scheme for power converters individually, a systematic FCS-MPC design framework is proposed. Firstly, the power converters are classified into associate converter groups based on isomorphic and dual relationships. Secondly, all converters in the associate group are modeled through unified models. Then, the same FCS-MPC framework can be shared between the converters with special relationships, which shows a significant simplification compared to the conventional design process. Also, the system performance of power converters in the associate group can be analyzed systematically. Various converters (e.g., three-phase current-source/voltage-source converter, single-phase T-type voltage-source converter) are selected as case studies in this work to show the feasibility of this work. Therefore, the systematic FCS-MPC design represents a universal design for a set of power converters while not only a specific one.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".