Enhanced Dynamic Regulation in Buck Converters: Integrating Input-Voltage Feedforward With Voltage-Mode Feedback
Bibliographic record
Abstract
DC-DC buck converters in automotive and aerospace applications are often required to handle large disturbances in their input supply and abrupt variations in their loads. This paper proposes a systematic method to combine input-voltage feedforward (IVFF) and voltage-mode feedback (VFB) controllers, aiming to enhance the closed-loop performance of these DC-DC converters. This method relies on the stability boundary locus approach to help select the proper control parameters that achieve strong dynamic stability across the full operating range regardless of practical implementation challenges. Also, an optimization approach is employed to minimize the passive components’ area within the compensator, achieving a 79% reduction in integration size compared to conventional designs. The controller was fabricated in a 0.35-μm CMOS technology, occupying a core area of 0.438 mm2. The prototype chip was experimentally tested to regulate a buck converter that leverages an e-GaN half-bridge while operating at 1 MHz. Measurement results show a remarkable closed-loop performance against line and load variations, reaching up to ±80 V/ms and ±535 mA per 150 μs, respectively. The output remains stable, showcasing very small (< 100 mV) to non-existent spikes and fast recovery periods. In addition, the system shows fast startup times (< 100 μs) with small overshoots (< 1%) observed at the output. The system power efficiency, tested across various loads, peaks at 95.14% while operating at 695 mA load current. It is shown that the combined-controller approach entirely eliminates transient voltage spikes, offering up to 100% improvement in dynamic performance over a standalone VFB controller.
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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) | 0.001 | 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".