Adaptive Control of Non-affine and Uncertain Variable-Speed Variable-Pitch Wind Turbines Subject to Input Saturation through Nussbaum-Type Functions for Multi-input Systems with Unknown Directions
Bibliographic record
Abstract
Using Nussbaum-type Functions for Multi-Input Systems with Unknown Directions, adaptive controllers are proposed and developed for a class of wind turbines with uncertainties, unknown disturbance, and subjected to input saturation. RBF neural networks are used to approximate the bounds of uncertainties and unknown disturbances sources, while Nussbaum-type Functions are employed to address the unknown input directions. Moreover, to improve the design's practicality, generic n-order dynamics are considered where there are n non-affine control inputs as opposed to widely used dynamics considering 1 non-affine input. Further, auxiliary saturation surfaces are adopted to guarantee the closed-loop system's stability in the presence of input saturation. In the end, the closed-loop system's stability is proven analytically, and simulations are rendered to visualize the analytical proof.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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".