Modeling of DC–DC Buck Converters for Large-Signal Frequency Response and Limit Cycles
Bibliographic record
Abstract
A zeroth-order-hold equivalent discrete-time model of the buck converter for computing its large-signal frequency response is developed and experimentally verified. It is shown that, with a dc bias and a sinusoidal variation of the input duty cycle, the frequency response of the output voltage from the converter shifts from underdamped behavior to damped behavior with increasing amplitude of the input sinusoid. It is observed that, with a given dc input bias and a given input amplitude beyond the range of the state-space linearized small-signal model, the converter behavior varies from exclusively continuous inductor current mode at low frequencies to behavior with continuous and discontinuous inductor current modes at high frequencies. The use of this sinusoidal input large-signal frequency response in predicting limit cycles induced by feedback of the output voltage using proportional and integral controllers for such converters is studied. Experimental results confirming the use of this large-signal frequency response are presented
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".