Modelling, simulation and validation of average current and constant voltage operations in non-ideal buck and boost converters
Bibliographic record
Abstract
DC-DC converters play a major role in a various applications in automobile engineering, portable electronics and LED drivers. In this work, basic converters like Buck and Boost converters operating in continuous conduction mode (CCM) considering the non-ideal parameters are modelled using volt-sec and amp-sec balance equations. The equations were simulated using MATLAB®/Simulink® software using appropriate step time, solver and the transients in inductor current and capacitor voltage were observed. Later, using the state space averaging (SSA) technique the transfer function of inductor current to duty ratio (Gid) and output voltage to duty ratio (Gvd) were derived. The parameters like low-frequency gain, gain margin (GM), phase margin (PM), crossover frequency, and stability were analysed using MATLAB software. It was found the non-ideal boost converter showed instability under constant voltage operation due to the presence of right half plane (RHP) zero. In order to validate the obtained transfer function using SSA, a new control technique called circuit averaging technique was used. The validation was performed using LTspice software tool. The frequency response of Gid and Gvd obtained using MATLAB and LTspice software tools showed a perfect match.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".