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 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".