Benchmarking the Performance of Boost-Derived Converters Under Start-Up and Load Transients
Bibliographic record
Abstract
The control of boost-derived converters presents a special challenge due to its non-minimum phase behavior, forcing a slow transient response when traditional linear techniques are employed. In order to improve the dynamic behavior, more complex controllers have been proposed (e.g., adaptive, nonlinear, boundary, etc.), but the improvements introduced by each one of them cannot be objectively assessed in the absence of a theoretical performance limit standard reference. This work introduces a benchmarking tool based on the evaluation of the converters' performance under start-up, load current, and input voltage step-up/step-down transients. Six performance indices are defined using the theoretical performance limits as reference. In this way, the converters' dynamic performance can be quantitatively assessed in six different aspects, enabling a thorough benchmarking procedure. The ideal parameters are found by exploring the performance limits of boost converters, providing valuable insight into the behavior of the topology. These reference parameters are found in a normalized domain and characterized by closed-form expressions, providing simple, intuitive, and general results valid for any combination of LC parameters and voltage levels. The characterization of the dynamic performance limits is validated by experimental results. The proposed benchmarking procedure is illustrated by evaluating the dynamic performance of two different dc-dc boost converters.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".