Smart Search Implemented H-Infinity Control Design for DAB Converter in DC Microgrid
Bibliographic record
Abstract
Dual active bridge (DAB) converters are used in dc microgrids for electric vehicle (EV) battery interfaces due to their bidirectional power transfer, high power density, and soft-switching capability. However, there are some challenges associated with these converters. On the one hand, substantial current stress and elevated rms current can result in substantial losses and safety concerns. On the other hand, external perturbations, disturbances, and variations in load can adversely affect the performance and stability of the system. To mitigate these issues, triple phase shift (TPS) modulation strategies have been introduced to reduce peak and rms currents, while$H_{\infty} $control methods have been developed to manage system uncertainties. An important aspect of the design of$H_{\infty} $control systems is the optimization of weighting function parameters, which is complicated by the complexity of the system. This work proposes a novel solution by using a revised nondomination-based genetic algorithm (NSGA)-II for$H_{\infty} $control, which facilitates the automatic determination of controller parameters efficiently with given optimization information. The proposed control method is capable of minimizing the peak or rms current, providing robustness against system uncertainty simultaneously. Simulation and experimental results are presented to demonstrate the robust performance and fast response times.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".