A novel parameter-independent digital optimal control algorithm for DC-DC Buck converters based on parabolic curve fitting
Bibliographic record
Abstract
Recently, optimal control on Buck converter for powering the latest computer central processor units (CPUs) has attracted more and more attention. In this paper, a novel digital control algorithm is presented to achieve the time-optimal response for dc-dc Buck converters without relying on any knowledge of converter design parameters such as output inductance, capacitance and even ESR value. This algorithm is based on the parabolic curve fitting analysis for deriving the algorithm formulas under the step load transients. Furthermore, this algorithm can be extended to adaptive voltage positioning (AVP) applications with simple modifications for a low ESR designed Buck converter. Also, it delivers a practical and cost-effective interface to AVP schemes due to the parameter-independent and current-sensorless detecting mechanism. Finally, simulations and experimental results of a 12 V-1.5 V Buck converter prototype are provided to validate the proposed schemes using digital signal processor (DSP) implementation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".