Model-Free Neural-Network-Based Adaptive Control for Single-Phase Dual-Active-Bridge Converter
Bibliographic record
Abstract
The Dual Active Bridge (DAB) DC-DC converter have several uses in current energy architectures, because of its numerous advantages, it is always possible to find the DAB in micro grids applications, energy storage systems applications, vehicles to grid applications, and a lot more. This wide range of applications subject the DAB to system variations and disturbances on both input side and output side, causing deficient performance of the DAB. This study proposes a model-free adaptive control based on feed-forward neural network, to control the output voltage of the DAB and to maintain it constant under system variation with finite time response. The proposed controller has the same layout as a PI controller. The study is done using MATLAB Simulink, where the system is tested under system variations. A performance test using time domain analysis is done for the proposed controller, a PI controller, and to a combination of an AANN in parallel with a PI controller. The comparison between the three controllers is concluded, and showed the upper hand for the proposed controller.
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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 | 0.001 | 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".