A novel modeling approach of LLC resonant converter for embedded controls
Bibliographic record
Abstract
Over the past decade, power converters have become more efficient (>96%), offering higher power density at reduced cost. This achievement is due to effective topologies, new components, and the deployment of embedded controls. One of the popular topologies for DC to DC conversion is the LLC resonant topology. Regulation of the LLC converter, operarting over wide range of frequencies, with high bandwidth and sufficient disturbance rejection capabilities is not without challenges due to varying dynamics behavior of the LLC. This calls for non-linear and/or adaptive control deployment through embedded controls. Extensive research has been done, to date, in describing state space modeling for the LLC topology. In this paper, a popular approach of Extended Describing Functions is applied to the LLC converter is analyzed. A new approach of estimation of the LLC dynamics based on Least Squares Method is proposed The proposed algorithm is applied to the simulation model of the LLC. The paper presents the modelling approach, simulation results and assesses the results for future development of adaptive control.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".