Robust Stall Flutter Suppression Using ℋ2/ℋ∞ Control
Bibliographic record
Abstract
Following the current trend in aeroelastic optimization, as wing structures have been made more flexible, active control systems such as flutter suppression systems have been widely adopted to reduce undesirable aeroelastic behaviors. The stability and the performance of flutter suppression control systems can be negatively affected as the inflow speed deviates from the nominal design value. In this work, a mixed-norm robust controller is designed to perform stall flutter suppression. A 2-dimensional nonlinear time-domain aeroservoelastic model is developed. The nonlinear equations are linearized at different flight conditions and are employed to construct an uncertainty model, which affects the nominal dynamics in an affine way. The obtained uncertain model of the aeroservoelastic system is used to design a mixed-norm H2/H∞ controller. The performance of the designed controller is compared with the performance of a non-robust H2 controller at different flight conditions. The proposed control architecture reduces the adverse effect of inflow speed variation on the performance of the closed-loop system.
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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 | 0.000 |
| Open science | 0.000 | 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 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".