Hardware efficient auto-tuned linear-gain based minimum deviation digital controller for indirect energy transfer converters
Bibliographic record
Abstract
This paper introduces a robust, hardware-efficient auto-tuned digital controller applicable to various hard switching dc-dc converters, including indirect energy transfer topologies. Unlike existing fast transient controllers for indirect energy transfer converters, the controller achieves fast transient response and practically minimum deviation of the output voltage without depending on information about converter parameters, i.e. inductor and output capacitor values. This is achieved by utilizing an auto-tuned non-linear controller that, based on the load-step information during a transient, finds the switching sequence for the converter to ramp up/down the inductor current to its new steady state average value in a single on/off switching action. Experimental results obtained from a 1.5 V to 3.3 V, 1A, 500 kHz boost prototype verify response with practically minimum output voltage deviation and demonstrate a more than 50% reduction of both output voltage deviation and recovery time compared to a voltage mode, fast PID-based controller.
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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".