Bibliographic record
Abstract
Due to the high power consuming characteristic of the next generation processors, the regulation requirement for power converter systems becomes more and more demanding in industry. Since it has the advantages of good programmability, flexibility and reliability, digital control system has the potential to achieve high performance control of DC-to-DC converters. The motivation of this research is to explore new digital control strategies for DC-to-DC converters to achieve high dynamic performance control. Four advanced digital control algorithms are explored in this thesis. The first new digital control algorithm for DC-to-DC converter is current mode fuzzy logic controller (FLC). Using the inductor current feedback in FLC, the proposed algorithm combines the merits of current mode control and fuzzy control. Simulation and experimental results verify the effectiveness of the proposed method. Secondly, extended state observer (ESO) is proposed to further improve the dynamic performance under load change. Based on nonlinear feedback, ESO can accurately estimate and compensate for the system external and internal disturbance, such as load change and parameter variation. Experimental results show that the proposed current mode FLC with ESO has improved the dynamic performance under load current change a lot and achieves the robustness to the system parameter variation. To achieve the best possible transient performance of DC-to-DC converters under load current change, new digital optimal control algorithms are developed thirdly. The proposed optimal algorithms accurately predict the minimum number of switching cycles and their duty cycle values for the converter system to recover to the steady state when load current changes. Therefore, minimum overshoot/undershoot and shortest recovery time are achieved. Experimental results verify that the proposed optimal algorithms produce much better dynamic performance than the conventional current mode PID controller. Finally, a new two-switching cycle compensation algorithm is proposed to generate the two-switching cycle duty cycle series to drive the converter to the steady state when input voltage changes. As a result, small overshoot/undershoot and short recovery time are achieved. Experimental results have proved that this algorithm has improved the dynamic performance under input voltage change a lot.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".