Multirate Finite-Control-Set Model Predictive Control for High Switching Frequency Power Converters
Bibliographic record
Abstract
Due to the modulator free structure, the finite-control-setmodel predictive control (FCS-MPC) needs a high sampling frequency/interrupt frequency (20–50 kHz) for power converters application, while the typical converter switching frequency is around 20–25% of the sampling frequency. To obtain a high switching frequency, it is not always practical to increase the sampling rate by considering the computational burden in a digital processor. Therefore, increasing the switching frequency without using a high sampling frequency is a critical task for FCS-MPC, particularly applied to silicon carbide and gallium nitride-based high switching frequency power converters. To solve this problem, this article proposes the multirate FCS-MPC (MRFCS-MPC), where the control frequency is allowed to be higher than the sampling frequency. Consequently, the switching frequency can be significantly increased without changing the sampling frequency. The proposed scheme inherits the ability to handle complex control objectives from the traditional FCS-MPC. The lifting model is built to predict the fast rate information of state variables based on the low sampling output. Then, the fast rate control inputs within one sampling interval are solved efficiently with a good tradeoff between computational burden and optimized system performance. The experimental motor drive system tests are carried out to verify the effectiveness of the proposed MRFCS-MPC.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".