Fractional Phase Lead Compensation RC for an Inverter: Analysis, Design, and Verification
Bibliographic record
Abstract
Repetitive control (RC) can offer a promising accurate voltage control scheme for constant-voltage constant-frequency (CVCF) pulse width modulation (PWM) inverters to compensate the harmonic distortion caused by nonlinear loads. However, limited by digital sampling, conventional RC with integer phase lead compensation cannot exactly compensate the system phase lag, which may result in instability in the case of low sampling frequency. In this paper, a fractional phase lead compensation RC (FPLC-RC) scheme is proposed to enable the phase lead step to be fractional, which can enlarge the stability region and improve the tracking accuracy. A newly devised finite-impulse response fractional lead filter based on Lagrange interpolation is applied to approximate the fractional lead items. Meanwhile, the synthesis and analysis of fractional phase lead RC for a single PWM inverter are given. Furthermore, simulations and experiments are provided to demonstrate the validity of the proposed FPLC-RC scheme.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".