Development and verification of real-time controllers for the F/A-18 vertical fin buffet load alleviation
Bibliographic record
Abstract
Twin-tail fighter aircraft such as the F/A-18 may experience intense buffet loads at high angles of attack flight conditions and the broadband buffet loads primarily excite the first bending and torsional modes of the vertical fin that results in severe vibration and dynamic stresses on the vertical fin structures. To reduce the premature fatigue failure of the structure and to increase mission availability, a novel hybrid actuation system was developed to actively alleviate the buffet response of a full-scale F/A-18 vertical fin. A hydraulic rudder actuator was used to control the bending mode of the fin by engaging the rudder inertial force. Multiple Macro Fiber Composites actuators were surface mounted to provide induced strain actuation authority to control the torsional mode. Experimental system identification approach was selected to obtain a state-space model of the system using open-loop test data. An LQG controller was developed to minimize the dynamic response of the vertical fin at critical locations. Extensive simulations were conducted to evaluate the control authority of the actuators and the performance of the controller under various buffet load cases and levels. Closed-loop tests were performed on a full-scale F/A-18 empennage and the results validated the effectiveness of the real-time controller as well as the development methodology. In addition, the ground vibration test demonstrated that the hybrid actuation system is a feasible solution to alleviate the vertical tail buffet loads in high performance fighter aircraft.
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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 | 0.002 |
| 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) | 0.002 | 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".