Computationally Efficient MPC Technique for PUC-Based Inverters Without Weighting Factors
Bibliographic record
Abstract
This paper introduces a novel efficient finite control-set model predictive control (EFCS-MPC) method with a single closed-loop control objective for various multilevel packed U-cell (PUC) inverter topologies. By employing the available redundancies of a PUC inverter switching combinations, the proposed EFCS-MPC integrates flying capacitors’ voltage regulation into the optimization process rather than the cost function. Therefore, considering the selection of a suitable switching state at each sampling time, in addition to having a minimized cost function, an intuitive control algorithm regarding the measured voltages of capacitors is also designed and considered as a benchmark. The introduced EFCS-MPC has several merits such as significantly reduced computational burden without requiring weighting factors selection as well as reliable steady-state and transient performance. These features make this hybrid control method an attractive option especially for industrial applications of PUC-based inverters. Extensive simulation results and analysis are also presented to demonstrate the operation of the proposed EFCS-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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".