Design and Control of a Novel Cuk-Boost Converter Using State Space Averaging Technique with Dynamic Bond Graph Modeling
Bibliographic record
Abstract
Today, designing a reliable and efficient step-up converter with high voltage gain for Photovoltaic applications has been a major concern for designers. This work proposes a fully soft switched high step-up converter that combines synchronous Cuk and Boost converters utilizing the shared component approach. The advantages of this converter compared with traditional Cuk and Boost converters are higher voltage gain and zero switching loss. A PSpice simulation was done at 225w power, 24 V input voltage, 150 V output voltage, and 100k Hz switching frequency to verify the proposed converter's functioning and the efficiency 96.56% has been achieved at mentioned operating power. Additionally, a bond graph model is designed to examine the dynamic behavior of the converter as it is presented, taking into account parasitic parameters and the specifications of realistic components. The bond graph model's simulation results have been explored. In the final section of this study, a PID controller employing the state space averaging approach is designed using SISOTOOL in MATLAB. Additionally, a Simulink model of the closed loop proposed converter is designed, and simulation results are reported.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".