Critical Parameters in the Transient Response of Synchronous Buck Converters
Bibliographic record
Abstract
The transient behavior of synchronous buck converters, using critical parameters, is investigated in this work under high current slew-rate load transients. Critical parameters are defined as the combination of parameters for which the peak transient response is dominated by the effect of the capacitor Equivalent Series Resistance (ESR). Given the importance of the effect ofESRin low-voltage, high- current synchronous buck converters, an effective normalization method is developed in this paper to reduce the dependence on other parameters, thus providing valuable generality to the transient analysis. A set of critical normalized expressions valid for any possible synchronous buck converter is derived, which can be used as a useful design tool to meet transient specifications. By using critical parameters, both transient recovery time and peak voltage regulation can be met while keepingLandCto a minimum. A detailed characterization of the effect ofESRis presented, and the normalized approach clarifies the role ofESRin the transient response. A critical design example is presented where the parameters of the converter are obtained by performing a simple denormalization. Finally, the critically designed converter is evaluated using boundary controllers as well as classical current- and voltage-mode controllers to assess the influence of the control scheme on the transient response of the converter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".