Model Predictive Control of Non-Isolated DC/DC Modular Multilevel Converter Improving the Dynamic Response
Bibliographic record
Abstract
The model predictive control (MPC) is a well-accepted method for controlling power electronic converters. This paper presents a tailored MPC approach in which the internal and external dynamics of the dc/dc modular multilevel converter (MMC) are integrated into the MPC algorithm. The proposed MPC approach introduces three control objectives to have full control over the internal and external dynamics. Each of the designed cost functions includes one primary term regulating one of the control objectives and one secondary term improving the converter performance. Unlike the conventional control approach based on multiple proportional-integral (PI) controllers, the proposed approach provides a straightforward way to design the control parameters. The operation of the presented MPC approach is thoroughly investigated and compared to that of the PI-based controller. Comparative simulation studies confirmed that the proposed MPC approach, compared to the conventional PI-based control, reduces the ac circulating current in the steady-state operation. In the transient mode, the MPC approach offers much smoother and faster responses to the changes in the power reference. The performance of the dc/dc MMC controlled by the proposed MPC approach under parametric uncertainty is investigated, and improved performance is obtained compared to the conventional PI-based control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".