Novel H-Infinity Controller for DAB Converter Under Constant Current Load
Bibliographic record
Abstract
Dual active bridge (DAB) converters have gained significant attention in DC microgrids, particularly for applications such as electric vehicles, energy storage systems, and uninterruptible power supplies, due to their capabilities for bidirectional power transfer, high power density, and inherent soft-switching operation. Despite these advantages, DAB converters face critical challenges, including excessive current stress and high RMS currents that lead to increased losses and potential safety risks. Moreover, external disturbances, perturbations, and variable loads can severely impact system performance and stability. To address these challenges, this article utilized an innovative control strategy lever-aging a novel NSGA-II-based H-infinity algorithm. This approach efficiently automates the optimization of controller parameters, simultaneously minimizing peak and RMS current levels while enhancing robustness against system uncertainties under constant current load (CCL) conditions. Comprehensive simulation validations demonstrate the effectiveness, robustness, and rapid response characteristics of the proposed control method.
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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) | 0.001 | 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 | 0.001 |
| 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".