Controlled current source circuit (CCSC) for Reduction of output voltage overshoot in Buck converters
Bibliographic record
Abstract
In this paper, an auxiliary circuit is presented to improve the dynamic response of a buck converter. Since it is well established that for typical voltage regulator applications, voltage overshoots (due to step-down load transients) are much larger than voltage undershoots (due to step-up load transients), the goal of the proposed method is to reduce the former. The circuit only functions during step-down load transients and operates by rapidly transferring excess load current from the output of the buck converter to its input. Unlike previous unloading auxiliary circuits, the proposed method uses a controlled current source circuit (CCSC) to remove a constant regulated current from the output. The CCSC has the following advantages over previous circuits: a) predictable behavior allowing for simplified design, b) inherent over-current protection, c) low peak current to average current ratio allowing for use of smaller components. Through selection of the auxiliary current, it is possible to obtain a balanced overshoot/undershoot response for a buck converter, significantly reducing the required output capacitance. In this paper, it is shown through analysis, simulation and experimental results that for a modest increase in component cost, a large reduction of voltage overshoot and output capacitor requirement can be realized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".