Enhanced average current-mode control for DC-DC converters based on an optimized fuzzy logic controller
Bibliographic record
Abstract
This paper proposes a novel optimized fuzzy logic controller (OFLC) for current-mode control (CMC)-based voltage regulation in DC-DC converters. The implementation of the proposed fuzzy logic controller (FLC) does not depend on the mathematical model of the converter and only requires knowledge about its operation modes. The fast and efficient algorithm of particle swarm optimization (PSO) has been employed to enhance the performance of the FLC by fine tuning its membership functions. The reference current for the current control loop has been obtained in the outer voltage loop by measuring load current and output voltage and calculating the load resistive impedance. The proposed OFLC has been used in four different case studies for evaluating its performance under different conditions of input voltage and load variations as well as variations in the system parameters, e.g. inductance and capacitance of the converter circuit. The results of simulations and comparison to a proportional-integral (PI) controller demonstrates the fast and robust performance of the proposed controller, able to effectively regulate the output voltage in presence of high perturbations in the input voltage and high variation of the converter components nominal values.
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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 | 0.000 |
| 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 | 0.001 | 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".