Aeroservoelasticity of an Airfoil with Parametric Uncertainty and Subjected to Atmospheric Gusts
Bibliographic record
Abstract
This paper presents the dynamics and adaptive control of an airfoil with structural stiffness and damping uncertainties, which is subjected to atmospheric gusts. The motion of the airfoil is modeled by three degrees of freedom (DOFs), namely, pitch, plunge, and flap. A flat spot or dead-zone-type stiffness is used for modeling the flap hinge free play. The nonlinear dynamics of the system without control and parametric uncertainty, where a cubic stiffness for pitch and a linear stiffness for plunge are considered, is examined. Numerical results show that the airfoil may become unstable via a Hopf bifurcation at a flow velocity well below the linear flutter speed; if the structural damping is not sufficiently high, it may also undergo chaotic motion. It was found that a proportional–derivative controller based on the partial feedback linearized system could effectively alleviate oscillations induced by gusts at flow velocities below and above the linear flutter speed. Next, an uncertain th-order polynomial stiffness for pitch and uncertain structural damping (modeled by viscous damping) coefficients for all DOFs are assumed. Considering such uncertainties, an adaptive controller with an estimation update law is designed to stabilize the airfoil subjected to gusts. A Lyapunov function is established to prove the stability of the closed-loop system. Simulation results demonstrate the effectiveness of the designed controller.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".