A Direct Current Control Scheme With Compensation Operation and Circuit-Parameter Estimation for Full-Bridge DC–DC Converter
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Bibliographic record
Abstract
To boost the dynamic performance of the full-bridge (FB) dc-dc converter, some model-based controls have been recently proposed in the literature. However, in terms of dynamic performance, these methods have failed to reveal the potential of this converter since the inherent relationship among the duty ratio, the input voltage, the output voltage, and the load resistor is not adopted in the controller, and the dynamic performances are still relayed on the fuzzy adjustment based on the disturbances of output voltage as the single-voltage-loop control method. In this article, based on this inherent characteristic of the FB dc-dc converter, an accurate transferred current modulation method is presented first. Based on this modulation method, a simple direct current control (DCC) scheme is proposed for a fast dynamic response with the feedback values of input voltage and load current. Moreover, the model uncertainties such as the power losses and control delay may influence the control performance. Therefore, a compensation operation is also presented to reduce the impact caused by these uncertainties to ensure the dynamic performance of the proposed strategy. In addition, since the circuit parameters including the leakage inductance and the output capacitor should be employed to implement the proposed DCC strategy with compensation operation, inaccurate circuit parameters may influence the performance of these proposed strategies. Therefore, the circuit-parameter estimation methods for these two circuit parameters are also proposed to ensure the dynamic performance of the FB dc-dc converter. Finally, the simulation and experiment results have verified the correctness and effectiveness of the proposed DCC strategy.
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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