A Robust One-Cycle Controlled Full-Bridge Series-Parallel Resonant Inverter for a High-Frequency AC (HFAC) Distribution System
Bibliographic record
Abstract
Resonant inverters are connected to a high-frequency AC (HFAC) bus, where power is delivered to different locations for points-of-use power management. Such a power distribution system subjects to more perturbations and load uncertainties than inverters operating with single load. A novel voltage control method is proposed in this paper for a high-frequency full-bridge resonant inverter with series-parallel resonant tank. A modified one-cycle controlled phase-shift modulation is proposed to effectively compensate the input line variations. The uncertainty model of the high frequency resonant inverter is developed and analyzed with the resonant circuit component tolerance, input line and load variations taken into design considerations. The voltage feedback controller is designed based on the Hinfinrobust control theory and is implemented with analog discrete devices. The proposed control scheme has the advantages of fast response for both input line and load perturbations. It also ensures a wide range of system stability and guarantees robustness of the power converter. Both simulations and experimental results are provided to verify with the theoretical analysis through an experimental prototype of a full-bridge resonant inverter with an output power of 150-W operating at 1 MHz and an output voltage of 28 V (rms).
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".