Novel concepts for power electronics control : introducing the dynamic physical limits, the average natural trajectories, and the centric-based controller
Bibliographic record
Abstract
Controllers are an essential component in power conversion systems that have a significant impact on characteristic features such as performance, efficiency, size, and cost, among many others. During the last four decades, countless efforts have been made to find better controllers for power electronics systems in order to improve the converters steady state and dynamic behaviour, increase power densities and reduce losses in the system. Small-signal based linear controllers have been the preferred alternative during decades. This technique features fixed switching frequency and low computation/sensing requirements, while the dynamic response can be improved to only a limited extent and the global stability cannot be ensured. On the other hand, excellent dynamic performances and global stability are achieved by boundary controllers, in which the switching frequency is variable and faster sensors are required. The first part of this work presents a practical tool which allows to objectively quantize improvements made by the controllers to the performance of power converters. The theoretical optimal dynamic behaviour of buck converters is determined, analyzed, and characterized using closed-form mathematical expressions, setting a strong benchmark point for the performance evaluation. Taking the physical limits of dynamic performance into account, and merging the advantages of linear and boundary techniques, a novel control scheme is developed for buck converters. The proposed controller is based on a large-signal model introduced here: the Average Natural Trajectories (ANTs). Enhanced dynamic performance and global stability are achieved while low sensing and computational requirements are maintained, which makes the technique very appealing for use in high-volume production applications. Due to the outstanding results in the basic buck converter, and in order to illustrate the application of the ideas introduced in this work for different topologies, the ANTs and the centric-based controller are developed for boost converters. The obtained results confirm the enhanced dynamic response and fixed frequency operation as natural advantages of the proposed control scheme. The theoretical findings are supported by detailed mathematical procedures and validated by experimental results, which highlight the practical usefulness of the concepts introduced in this work.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".