An adaptive current mode fuzzy logic controller for DC-to-DC converters
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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 an 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. The dynamic performance of power converter system is improved. Furthermore, in order to enhance system robustness and adaptability, a new nonlinear configuration called extended state observer (ESO) is developed. By using ESO, the influence of load disturbances and parameter changes are precisely estimated and compensated without accurate knowledge of converter parameters. Simulation results have demonstrated that the proposed methods ensure good robustness and adaptability under modeling uncertainty and external disturbance, such as load current variation, supply voltage changes and converter parameter changes. It is concluded that the proposed topology produces substantial improvement of dynamic performances such as small overshoot, more damping and fast transient time under different operating conditions. 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.
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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 it