Generalized Predictive Control for $LC$-Filtered Voltage-Source Inverters With Enhanced Predictive Horizon
Bibliographic record
Abstract
Generalized predictive control (GPC) for power converter applications provides the benefits of constant switching frequency, explicit stability check, and high control bandwidth. However, featuring the second-order characteristic in the$LC$-filtered voltage source inverter, the assessment and investigation of GPC as the inner control loop have not been fully fulfilled, especially under the grid-connected mode. Besides, the inherent ability of GPC enables the prediction of future system behavior within longer horizons, which, however, has been rarely investigated and, thus, it is still unclear about the closed-loop property brought by the enhanced predictive horizons. To this end, this work first clarifies the contribution of adopting GPC by comparing it to the capacitor-voltage control+inductor-current feedback active damping with optimal damping coefficient design, where the virtual synchronous generator is used to implement the case study. Through the simulation and experimental results, the GPC expresses a smoother and faster dynamic process and enhanced transient frequency nadir compared to the existing multiloop strategy. The robustness and stability are analyzed through the closed-loop eigenvalue, and it is also revealed that the GPC achieves stronger robustness against the variation of the control and plant parameters simultaneously by increasing the prediction horizon length.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".