Analysis and Comparison of Frequency Stabilization Loops in Self-Oscillating Current Mode DC–DC Converters
Bibliographic record
Abstract
DC-DC converters, used in bidirectional applications such as battery/u-cap hybrid energy-storage systems, usually operate in current mode with a wide range of input and output voltages. Conventional current-mode control schemes, which requires adaptive slope compensation, are not attractive in these applications. In this paper, a class of self-oscillating hysteretic current-mode control (HCMC) schemes with frequency stabilization are analyzed and compared. Among them, the ripple HCMC scheme has the advantages of the inherent access to the average inductor current information, and inherent decoupling between the voltage and the frequency loops. The modeling and design procedure of two frequency regulation methods, namely the phase-locked loop (PLL) and delay-locked loop (DLL), are presented and compared in detail. The analysis is applicable to a range of inductive converters such as buck, boost, and buck-boost. HCMC implemented with a DLL is shown to not only offer comparable bandwidth compared to the PLL, but is also more robust against system parameter variations. The ripple HCMC controllers with PLL and DLL are implemented digitally, and the two schemes are verified experimentally on a 1 MHz 1-2.5 V to 5 V bidirectional boost converter prototype.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".