Adaptive ANN based Single PI Controller for Nine-Level PUC Inverter
Bibliographic record
Abstract
This paper presents an Adaptive Proportional Integral (API) control strategy to regulate the capacitors voltage and load current of Nine-Level Packed U-Cell (PUC9) inverter in stand-alone mode of operation. PI is a linear control method which has been widely used in power converter topologies due to simple implementation. However, applying PI method to the power converters as nonlinear systems causes several problems like steady state error, difficulties of control factors tuning and instability in presence of uncertainties and disturbances. In the proposed API method, a single PI controller is used to adjust both capacitors voltage and load current amplitudes. A Multilayer Perceptron (MLP) Artificial Neural Network (ANN) is also trained by Artificial Bee Colony (ABC) algorithm to adapt the capacitors voltage references so that eliminates steady state error of PI and stabilizes the PUC9 voltages and current in the presence of parameters variation. Simulation results obtained by MATLAB/Simulink confirm high performance of the API.
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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) | 0.002 | 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".