ANN Based Model-Free Sliding Mode Control for Grid-Connected Compact Multilevel Converters: An Experimental Validation
Bibliographic record
Abstract
Sensitivity to parametric uncertainties and mismatches is an unpleasant fact that significantly debilitates the reliability and the efficiency of model based controllers in dealing with power converters known as variable structure systems. The parametric sensitivity is adversely escalated in the case of multilevel converters since more active and passive components are used to increase the voltage levels. Conventional first order Sliding Mode Control (CSMC) is a simple but robust model based technique, which can dominate the parameters variations effects on the control loop stability by considering the parameters as bounded variables instead of constants. However, designing CSMC becomes difficult and chattering as a damaging phenomenon rapidly grows up in the presence of the parameters variations and disturbances. Accordingly, this paper is meant to investigate the possibility of modifying the CSMC equations using artificial neural network so that it becomes insensitive to the model uncertainties and mismatches. Several convincing simulations and experiments are also considered to assess the proposed model-free SMC technique in practice.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".