Boundary control for isolated topologies: The Natural Switching Surface for Full-Bridge ZVS
Bibliographic record
Abstract
This paper presents the use of a high performance boundary controller for a classic isolated topology: the Full-Bridge ZVS. An enhanced dynamic response is obtained by employing the Natural Switching Surface (NSS), which is thoroughly derived in the normalized geometrical domain. The advantages of the normalization are the simple graphical representation, the generality for any combination of parameters, and the mathematical simplicity. Recently, non-isolated basic topologies have benefited from advancements in boundary control. The analysis and derivation in this work bring the benefit of outstanding dynamic performance to this isolated topology. As demonstrated in this work, the relationship between the leakage and output filter inductances make possible the formulation of the natural trajectories for isolated converters. The resulting SS provides an excellent dynamic response during start-up, reference change, and sudden output loading conditions. Experimental results are presented to illustrate the characteristics and advantages of the control scheme, and the converter operation with fixed switching frequency.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".