Sampled-data Modeling of PWM Boost Converters in Continuous and Discontinuous Inductor Current Modes
Bibliographic record
Abstract
When the boost converter operates in continuous and discontinuous inductor current operation modes, owing to the relatively more complicated nature of the boost converter with non-ideal elements, a parsimonious large signal and small signal model for this converter, with and without feedback, is lacking. In this work, a zero order hold (ZOH) equivalent sampled-data (discrete-time) model of the boost converter for computing its small-signal frequency response and closed loop behavior for both large and small signals is developed and experimentally verified. In this model, non-ideal conductive loss effects can also be easily taken into account in continuous and discontinuous inductor current modes. Frequency response computation technique from the model are developed and a Newton-Raphson technique is shown to accelerate the computation of the frequency response. Experimental evaluation of the predicted small-signal frequency response from the model is presented and the computational efficiency of the Newton-Raphson technique is evaluated. Using setpoint perturbations, the ability of the discrete model in capturing nonlinear closed-loop (PI) control performance is demonstrated with experimental confirmation
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".