An optimal control strategy for digitally controlled single-phase power factor correction AC-DC boost converter
Bibliographic record
Abstract
AC-DC converters are widely used for conversion of utility ac input to a dc voltage suitable for telecom and other dc loads. There is dedicated analog control ICs available for controlling such converters and to accomplish their input power factor correction. Presently with decreasing cost of DSPs they are being applied more and more for controlling power electronic converters. This paper presents a simple and novel digital control scheme for a power factor correction (PFC) AC/DC boost converter based on optimal control theory. It is shown in this paper that the dynamics of the boost converter can be significantly improved by implementing the proposed controller, which introduces a Lyapunov function to the system and determines the optimal converter duty-ratio needed to minimize the Lyapunov function. The performance of the proposed controller is confirmed on a digitally controlled 3 kW boost PFC converter operating at 100 kHz switching frequency. Experimental results show that the proposed controller causes the boost converter to operate with low input current harmonics and very fast output voltage transient responses that are much less than a single input line cycle, compared to responses of several input line cycle transient periods seen in conventional control schemes.
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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 | 0.001 | 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".