Lyapunov Based Neural Network Estimator Designed for Grid-Tied Nine-Level Packed E-Cell Inverter
Bibliographic record
Abstract
This paper presents a robust controller based on Lyapunov Control Theory (LCT) and Artificial Neural Network (ANN) for the grid-tied single-phase nine-level Packed E-Cell (PEC9) inverter. The proposed robust controller is designed using nonlinear model of the grid-tied PEC9 in which a proper negative definite function with a Positive Weighting Factor (PWF) obtained by LCT guarantees the designed controller to provide globally asymptotically stability for the system in unsteady conditions. An online ANN Estimator (ANNE) is also designed to update the PWF which has the most effect on controller performance. ANNE is also trained through an online optimization loop based on Artificial Bee Colony (ABC) algorithm that reduces the training time dramatically and enhances precision of the trained estimator. Some experimental and simulation tests are accomplished by dSpace-1104 hardware and MATLAB/Simulink software to confirm the high accuracy and fast transient response of the proposed LCT-ANNE in the current reference tracking and obtaining low injected current THD for both steady-state and variable conditions of the grid-tied PEC9.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".