Development of robust control law for active buffeting load alleviation of smart fin structures
Bibliographic record
Abstract
Aerodynamic buffeting load can lead to premature fatigue damage of aircraft vertical fin structures. This article presents a robust control law development strategy for active buffeting load alleviation of a smart fin structure. The impact of aerodynamic loads on the modeling uncertainties of the smart fin was investigated through extensive wind tunnel tests. Test results revealed that the airflow introduced higher damping ratio and caused frequency shift to the vibration modes. These aerodynamic effects may adversely affect the performance and robustness of active control laws. Based on the observations, the structured singular value synthesis technique was used to develop a robust control law for the smart fin using a truncated baseline dynamic model. A parametric uncertainty block was introduced to account for the changes in the modal parameters of the baseline dynamic model due to the aerodynamic effects. An additive uncertainty block was included to account for the unmodeled higher-order vibration modes as well as the modeling errors in the low frequency range. The robust performance of the control law was demonstrated through simulations as well as extensive closed-loop control experiments in the wind tunnel using various free airstreams and vortical airflows. This provided a verified control law design strategy for active buffeting alleviation applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".