Dual-Loop Controller for <i>LLC</i> Resonant Converters Using an Average Equivalent Model
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Bibliographic record
Abstract
LLC resonant converters have gained popularity in a wide number of industrial applications due to their high efficiency and power density. Common applications of these converters are battery chargers, electric vehicles, and high-efficiency power supplies, which require tight output voltage regulations. However, traditional averaging techniques employed in pulse width modulation converters cannot be employed for LLC s. As a consequence, designing linear controllers for these types of converters requires complex analysis or applying empirical methods. This paper proposes a simple and straightforward methodology for designing linear controllers for LLC resonant converters. A dual-loop control scheme including an inner current loop and an outer voltage loop is introduced. A simplified second-order equivalent circuit is employed to derive all the relevant equations for designing the compensators. Simulations and experimental results using a 150-W platform are employed to validate the theoretical analysis.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it