Control of wing rock phenomenon with a variable universe fuzzy controller
Bibliographic record
Abstract
Wing rock is a highly nonlinear phenomenon in which aircraft undergo limit-cycle roll oscillations at a high angle of attack (AOA). It is a challenge to design an appropriate controller, especially with modeling errors and external disturbances. The methodology of fuzzy logic control (FLC) appears very useful when a process is too complex or when an available source of information is interpreted qualitatively, inexactly, or uncertainly, but we also note that the FLC of a process under disturbances usually exhibit a tracking error when the controlled system tends to steady state. A variable universe fuzzy control design approach is utilized to improve both tracking precision and robustness of fuzzy PD control. A switching mechanism is developed to achieve this control scheme: when the tracking error is in a large range, fuzzy PD control is used to keep fast adjustments and to reduce the error; when the tracking error is in a small range, variable universe fuzzy control is then used as a fine controller to eliminate the error. Simulation studies for the nonlinear wing-rock control show that the new control scheme is a powerful tool to improve control system performances.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".