Adaptive Spatial Filtering for Aeroservoelastic Response Suppression
Bibliographic record
Abstract
Aeroservoelastic interactions have become a critical design consideration for meeting the increasingly demanding performance requirements imposed on aircraft designs. The traditional approach for establishing flight control system (FCS) stability margins is to use notch-filtering; this introduces phase lag that limits the FCS bandwidth, and may not be robust to changes in flight condition, aircraft configuration, or damage. We propose an adaptive spatial filtering approach that makes use of additional sensors to reduce aeroelastic interactions with the flight control system, allowing for increased control bandwidth, and greater robustness. A simple, computationally-efficient, and robust adaptation algorithm is used to optimize the spatial filtering as the system changes. A Lyapunov function is used to prove stability of the combined FCS and adaptive filter. The adaptive spatial-filtering approach is demonstrated on a simple aeroelastic model of a Boeing 747-SP, yielding attenuation of target modes of 20dB and higher without the phase lag associated with time-domain notch and low-pass filters. The ability to detect and track changes in the system is demonstrated. The adaptive spatial filter can be used in any application where minimizing control interactions with uncertain or time-variant structural dynamics is desired.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".