Multiobjective Model-Free Predictive Control for Motor Drives and Grid-Connected Applications: Operating With Unbalanced Multilevel Cascaded H-Bridge Inverters
Bibliographic record
Abstract
A multiobjective model-free predictive control (MO-MFPC) strategy is proposed in this article for multilevel cascaded H-bridge (MLCHB) inverters with unbalanced conditions. The compensated current variation (CCV), which allows the inclusion of the proportional and integral terms into the cost function, is generalized to improve the accuracy of MFPC over a wide range of applications. The new extended CCV is used to define the voltage control objective without involving the output voltage model of MLCHB. This voltage control objective is used to evaluate all state candidates to achieve a suitable subset for the current control objective. To achieve a better tradeoff between the current accuracy and the injected common-mode voltage (CMV), CMV is added to the cost function. Simulations and experimental evaluations show that, compared to existing MFPCs and classic model predictive control for MLCHB inverters, MO-MFPC achieves a better current accuracy over a wide range of applications and unbalanced MLCHB operating conditions.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".