Dual-loop geometric-based control of boost converters
Bibliographic record
Abstract
Voltage mode and dual-loop current-mode linear schemes are widely used for controlling boost converters due to their simple implementation and fixed frequency PWM operation. Nevertheless, the dynamic response of the voltage loop is usually limited by the characteristic non-minimum phase behaviour of the boost topology. On the other hand, excellent dynamic performances can be achieved with boundary controllers in which the demands placed on processors and sensors are greatly increased. A novel trajectory-based dual-loop control scheme is introduced in this work by combining linear techniques with state-plane analysis. Geometric trajectories are employed to define the outer voltage loop, allowing to achieve fast and reliable dynamic response. The well-known issues caused by the right-half plane zero are solved by defining the outer voltage loop control using simple geometric equations. The proposed trajectory-based voltage loop tightly controls the time-domain evolution of the state variables, providing a reliable transient response by following a desired geometrical path to reach the desired steady state operating point. Straight line and circular trajectories are implemented resulting in an outstanding, well defined, and reliable transient behaviour. Experimental results of dual-loop geometric-based controlled 100W platform validate the proposed control concept and highlight the strong contribution to the theoretical and applied fields 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 | 0.001 | 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".