Design of a Nonlinear Controller For a Two-Stage DC-AC Converter For DC-Link Drive Applications
Bibliographic record
Abstract
As electric machine drives are growing in complexity, controlling the dc-link voltage within their drive train has become an increasing challenge. This paper implements a nonlinear peak current mode (PCM) controller in a dc-dc converter in drive applications. Unlike conventional PCM controllers which rely on constant or linear slope that is generated by the converter’s inductor current to control its output voltage, the piecewise quadratic slope (PQS) utilizes a nonlinear piecewise quadratic compensation signal that provides a wider range of operation and higher immunity against disturbances. In this paper, a comparative study is conducted between the nonlinear PQS controller and a conventional voltage mode controller. A two-stage dc-ac drive train consists of a dc-dc boost converter and a 3-leg 3-phase inverter driving an induction machine (IM). The IM is used as a test bed for both controllers. The simulation results show that the PQS controller not only reduces system oscillations, overshoot/undershoot, and settling time, but also helps in mitigating protentional converter failures during transients by stabilizing its internal dynamics.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".