Adaptive Control of Four-Quadrant DC-DC Converters in Both Discontinuous and Continuous Conduction Modes
Bibliographic record
Abstract
The inherently different dynamics of a DC-DC converter while operating in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) necessitate an advanced controller to control the inductor current. A conventional PI controller cannot be used across both modes since it does not guarantee a smooth transition between both modes. Furthermore, in time-varying input-output voltage applications of the four-quadrant converter such as in battery charging applications, the location of the boundary between the CCM and the DCM changes dynamically, creating an uncertainty. Therefore, a robust controller is required to accurately track the inductor current in the presence of uncertainties. Thus, an adaptive controller is proposed in this work, which is based on the general inverse model of the four-quadrant converter in both modes. Moreover, gain scheduling is used to switch the parameters of the controller as the converter transits between the DCM and the CCM. The adaptability and effectiveness of the controller in ensuring a smooth transition is validated by numerical simulations conducted on various converter topologies. Experimental results are also presented for a buck converter.
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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 | 0.000 |
| Open science | 0.001 | 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".