Extreme start-up response of LLC converters using average geometric control
Bibliographic record
Abstract
Resonant power converters have become ubiquitous to efficiently process electrical energy, however, their complicated structure challenges linear controllers in anticipating their large signal performance. Since small-signal modeling techniques do not provide sufficient information regarding the large signal behavior of power converters, nonlinear geometric controllers can be employed to tackle the poor large signal dynamic response. In this paper, a nonlinear controller called the average geometric controller is introduced for the LLC converter with the ability of providing an extreme start-up dynamic response and eliminate overshoot. A novel average large signal model is developed and serves as the foundations to develop the average geometric controller for LLC converters. The average large signal model significantly simplifies the large-signal behavior analysis and results in obtaining the average circular trajectories of the LLC converter. In order to validate the theoretical analysis, experimental and simulation results of a 500W prototype LLC converter employing the average geometric controller are presented. The experimental results show a significant increase in start-up performance while the overshoot is eliminated.
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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 | 0.000 |
| 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 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".