Fuzzy Generalized Predictive Control of Power Converter in DC Microgrids with Constant Power Load
Bibliographic record
Abstract
This paper develops a Takagi-Sugeno Generalized Predictive Controller (TS-GPC) to adjust the power converters of a class of direct current (DC) Microgrids (MGs) feed linear resistive loads and nonlinear Constant Power Loads (CPLs). Since the overall DC microgrid has a nonlinear behavior, it is vital to model the behavior of the system with an accurate model and design the control input. In this regard, a Takagi-Sugeno fuzzy model is utilized to model a nonlinear system with multi-linear models accurately; and, a GPC is applied on the multi-linear models based on the obtained model and defined cost function. The numerical results show that the model can predict the behavior of the nonlinear system and the proposed controller can effectively track the desired reference.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".