Distributed Continuous Control Set Model Predictive Control Using Nash Equilibrium for Flying Capacitor Multilevel Converters
Bibliographic record
Abstract
This article presents a novel distributed model predictive control (DMPC) method for multilevel converters with multiple switching configurations. The central aspect of the control approach is to organize the dynamic game among multiple subcontrollers and achieve the Nash equilibrium to realize the deadbeat control of the multilevel converter. Compared with the conventional lumped structure finite control set model predictive control (FCS-MPC) method, DMPC is more flexible in structure and easy to expand. Moreover, the output of DMPC is duty cycle for modulation resulting in fixed switching frequency and improved ripple. The DMPC method has been experimentally validated with a 100 kHz GaN-based five-level flying capacitor (FC) converter. Experimental results show that compared with FCS-MPC, the current ripple, the voltage ripple, and the total harmonic distortion (THD) under DMPC control are reduced by 80%, 70%, and 90%, respectively. At the same time, the computational burden of DMPC is only 16% of that of FCS-MPC. These advantages would promote the implementation of fixed frequency and deadbeat nonlinear control of the FCMC at high levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".