Smart Search Implemented H-Infinity Control Design for DAB Converter in DC Microgrid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty} $ </tex-math></inline-formula> control methods have been developed to manage system uncertainties. An important aspect of the design of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty} $ </tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty} $ </tex-math></inline-formula> 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it