Self-Tuning Digital Current Estimator for Low-Power Switching Converters
Bibliographic record
Abstract
An inductor current estimator suitable for low-power digitally controlled switch-mode power supplies (SMPS) is introduced. The estimation of the average current value over one switching cycle is based on the analog-to-digital conversion of the inductor voltage and consequent adaptive signal filtering. The adaptive filter is used to compensate for variations of the inductance and series equivalent resistance affecting accuracy of the estimation. Based on the response to an intentionally introduced and known current step, the filter tunes its own parameters such that a fast and accurate estimation is obtained. A practical realization of the estimator resulting in a modest increase in digital controller complexity is shown. Besides a simple digital IIR filter and a load step circuit, it only requires a slow analog-to-digital converter for the input voltage measurement. The estimator is tested on a 6.5 V to 1.5 V, 15 W, digitally-controlled buck converter prototype. The results show that between 20 % and 100 % of the maximum output load the estimator has accuracy better than 10 % and one switching cycle response time.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".