Analysis of PID and Fuzzy Logic Control for an Interleaved Boost Converter in Fuel Cell Applications
Bibliographic record
Abstract
The increasing need for power conversion systems that are efficient and reliable in fuel cell systems has created the demand for advanced control methods to maximise the performance of power electronic converters.The efficiency of an interleaved boost converter (IBC) controlled by a conventional Proportional-Integral-Derivative (PID) controller and by a fuzzy logic controller (FLC) when used in fuel cell applications is compared here.Fuel cell systems leverage the benefits of topology in the IBC, including decreased input current ripple, increased efficiency, and enhanced power density.To solve the nonlinearities and uncertainties inherent in fuel cell dynamics, the FLC provides greater robustness and flexibility in terms of handling non-linearity and steady state error over the PID controller, which is the most used due to its simplicity and effectiveness.Dynamic response, efficiency, robustness to varying load conditions and changes in input voltage, and output voltage regulation are some of the performance parameters simulated with MATLAB/Simulink.From the simulation, the FLC-based IBC operations are superior to the PID-regulated IBC with lower overshoot, quicker transient response, and better disturbance rejection.This renders it more suitable for fuel cell operations where efficiency and operation stability are paramount.The results of this work, as described below, present useful guidelines on the design and selection of control schemes for guaranteeing maximum power converter efficiency in fuel cell-based battery charging systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".