Near-Time-Optimal Dynamics in PWM DC–DC Converters: Dual-Loop Geometric Control
Bibliographic record
Abstract
Traditional voltage-mode and dual-loop current-mode linear schemes are widely used for controlling the fundamental dc–dc converters due to their simple implementation and fixed-frequency. Pulse-Width-Modulation (PWM) operation. While the dynamic response can be improved by pushing the control bandwidth, lower stability margins may lead to unexpected peak deviations in the inductor current and capacitor voltage, causing failures due to magnetic saturation or excessive voltage overshoot. The concept of dual-loop geometric-based control is introduced in this article by combining geometric state-plane analysis for the outer voltage loop with traditional current-control techniques for the inner loop. The traditional linear voltage compensator is replaced by a geometric alternative that can control the time-domain evolution of the state variables, providing a fast and reliable transient response by following a desired geometrical path to reach the steady-state operating point. In this way, stringent dynamic requirements can be successfully addressed by shaping the state variables’ time evolution by employing a simple geometric equation to define the voltage compensator. Circular trajectories are implemented using simple parametric equations resulting in remarkably well-defined, reliable transient behavior. Experimental results of dual-loop geometric controlled platforms validate the proposed control concept and highlight the strong contribution to the applied field made by this innovative 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 | 0.000 | 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".