A novel fixed‐time dynamic surface DC/DC SEPIC converter controller loaded by uncertain buck‐based constant power loads
Bibliographic record
Abstract
Abstract The stability analysis of the single‐ended primary‐inductor converter DC/DC converters feeding constant power loads (CPLs) is of major importance. The nonlinear behaviour of CPLs and their negative incremental impedance, which impose adverse effects on system damping and stability margins, necessitate developing proper strategies for an efficient implementation framework of DC microgrid. In this paper, a fixed‐time sliding mode disturbance observer is first addressed to provide an estimation of the power flow moving along the uncertain CPLs with time‐varying nature within a fixed time. It not only expedites the estimation rate but also improves the robustness against physical parameter variation. The fixed‐time dynamic surface control law is then developed based on the estimated load power for the duty cycle of the switch such that the entire power grid becomes stable and the desired voltage of the DC bus is tracked within a fixed time irrespective of the initial conditions. A rigorous Lyapunov‐based approach is represented to guarantee the semi‐global fixed‐time uniform ultimate boundedness of the proposed scheme. Finally, in order to verify the proposed methodology's strengths, Model‐in‐the‐Loop real‐time simulations are performed under various operating case studies.
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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".