A new adaptive fuzzy logic control method for DC-to-DC converter
Bibliographic record
Abstract
This paper introduces a new fuzzy logic controller (FLC) using inductor current feedback for significantly improving the dynamic performance of DC-to-DC converters. Inductor current plays a very important role in high performance DC-to-DC converter control and FLC is suitable to deal with time-varying nonlinear nature of power converters. Based on the feedback of the inductor current, the new control method combines the merits of both the conventional FLC and current mode control. Furthermore, extended state observer (ESO) has been developed to ensure high dynamic performance of DC-to-DC converters. By using ESO, the influence of load disturbances and parameter changes are accurately estimated and compensated. The simulation results show that the proposed FLC with ESO ensures very good robustness and adaptability under modeling uncertainty and external disturbance, such as load current variation, supply voltage changes and converter parameter changes. In addition, small signal frequency response analysis demonstrates that by using the proposed FLC, the bandwidth and phase margin of the closed loop system have been significantly increased.
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.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".