Boundary Control With Corrected Second-Order Switching Surface for Buck Converters Connected to Capacitive Loads
Bibliographic record
Abstract
Boundary control (BC) with second-order switching surface is exploited for achieving faster response time and robust operation of switching power converters. However, the system performance of the boundary-controlled converters is significantly affected when it is connected to a second-stage converter with a large input capacitor. This article studies the shortcomings in second-order BC schemes of buck converter with capacitive loads and nonlinear switching loads. Second, this article proposes a BC scheme with corrected second-order switching surface to drive buck converters cascaded to boost converters. The switching criteria of the corrected control law account for the effect of unknown load capacitance as well as the variation in filter parameters. Therefore, an outer voltage ripple feedback loop is introduced to determine the corresponding switching criteria gain factor that adjusts the overall gain, while maintaining the output voltage ripple at a specified voltage band. The proposed method is verified by both simulation and hardware experiments. A 250-W buck converter prototype has been built to validate the control scheme under different load types, including a resistive-capacitive load, a boost converter, and a commercial dc electronic load. A comparison is drawn between conventional BC and the proposed method in both simulation and experimental environment, in order to highlight the advantages of the proposed method. With this approach, the converter operates at designed BC parameters independent of load capacitance and system parameter variations.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".