Fast and Smooth Control of Converter’s DC-Link Voltage Using Optimal Fractional-Order Interval Type-2 Fuzzy Controller
Bibliographic record
Abstract
Abstract The need to use fully controlled AC/DC converters for DC-based power supply systems has led to structural development and improved performance. Due to existence of various distribution network loads sensitive to noise and fluctuation, PWM-based converters cannot provide accurate and fast performance. In this regard, this paper suggests a novel robust control strategy based on FOIT2FC for this distribution power system to enhance the tracking accuracy and smooth the DC voltage of converter’s DC-link with presence of system uncertainties and disturbances. To strengthen the proposed control strategy, MOMSA has been utilized to optimally tune the FOIT2FC’s parameters. Precise and robust tracking performance of the proposed MOMSA-based FOIT2FC has been thoroughly compared with PSO-based FOIT2FC, PSO-based IT2FC and PSO-based Fuzzy controllers under DC reference variation, resistive load variation, unbalanced three-phase voltage and reproductive operation. The simulation results show that the proposed MOMSA-based FOIT2FC almost gives 1.53% overshoot, 1.25% undershoot and 0.023% fluctuation, while PSO-based FOIT2FC gives 1.95% overshoot, 1.89% undershoot and 0.14% fluctuation, PSO-based IT2FC gives 2.46% overshoot, 2.38% undershoot and 0.19% fluctuation and PSO-based Fuzzy controller gives 2.74% overshoot, 2.95% undershoot and 0.22% fluctuation. To more certify the simulation model, its prototype structure has been provided and tested in laboratory.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".