Modulated model predictive control (M<sup>2</sup>PC) with fixed switching frequency for an NPC converter
Bibliographic record
Abstract
Advances in power electronic converters technology along with the wide range of applications require sophisticated digital control schemes. In the recent years Finite Control Set Model Predictive Control (FCS-MPC) has been successfully proposed as an alternative to the traditional control techniques, due to its fast dynamic response, easy inclusion of nonlinearities and constraints in the control algorithm and the ability of incorporate several control requirements in one cost function. However, FCS-MPC is characterized by a variable switching frequency which can cause a degradation in system performance, or demand higher output filtering. In this paper a Modulated Model Predictive Control (M2PC) strategy is proposed for constant switching frequency operation and verified with a neutral-point-clamped (NPC) converter for load current regulation and DC-link capacitor voltages balancing. The aim of this control technique is to obtain a modulated waveform at the output of the converter, whilst maintaining all the desired characteristics of FCS-MPC. The feasibility of this strategy is evaluated using simulation and compared to the classical FCS-MPC strategy.
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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) | 0.000 | 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 | 0.001 |
| 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".