DC–DC Converter With Digital Adaptive Slope Control in Auxiliary Phase for Optimal Transient Response and Improved Efficiency
Bibliographic record
Abstract
In this paper, a digital adaptive slope control technique is presented to improve the dynamic response of a current-programmed mode (CPM) buck converter employing a low-cost auxiliary phase. The advantages and drawbacks of the existing analog and digital nonlinear control techniques are discussed in detail. The benefits of the proposed control scheme include superior voltage drop and settling time, adaptive slope control to accommodate a range of auxiliary phase inductor values, and on-line calibration to compensate for tolerance in the main phase and auxiliary phase inductance. The proposed technique is experimentally verified on a 500-kHz, 10-2.5 V CPM buck converter prototype. Charge balancing and optimal transient response are achieved for a range of positive and negative load steps. Operating the auxiliary phase under light-load condition is also demonstrated to further justify the use of the auxiliary phase. Compared to a representative single-phase converter, the proposed system not only has over two times improvement on the voltage drop and three times lower steady-state voltage ripple but also achieves approximately 2% heavy-load and 10% light-load efficiency improvement. The impact of the auxiliary phase operation on the converter's dynamic efficiency is also evaluated at different load step amplitudes and frequencies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".