Implementing adaptive digital control on an existing LLC DC-DC resonant converter
Bibliographic record
Abstract
In this paper, the implementation and design process of integrating an adaptive digital PID control scheme on an existing 650W LLC DC-DC resonant converter is presented. The objective is to improve the converter's dynamic response while maintaining adequate gain and phase margins over several different operating ranges and to improve the converter's ability to reject the 120Hz disturbance produced from the AC line source. The small signal models of the converter are obtained using MATLAB's System Identification Toolbox, which estimates a mathematical model based on the empirical uncompensated loop-gain frequency response data collected either from PSIM simulation or from the physical converter using a Venable frequency response analyzer. Compensator designs are based on the estimated models and digital control is performed using a TMS320F28035 digital signal microcontroller. Simulation and experimental results are presented which validates the estimated model and the compensator design process. The adaptive compensation control performance is compared to a typical single compensation controller to show the performance advantages.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".