Model-referenced Adaptive Flight Controller based on Recurrent Neural Network for the Longitudinal Motion of Cessna Citation X
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Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3797.vid This paper presents a methodology developed at the Laboratory of Applied Research in Active Controls, Avionics, and AeroServoElasticity (LARCASE) for the control of the longitudinal motion of a Cessna Citation X using a neural network-based adaptive flight controller. Herein, the incorporated aircraft model is originated from a simulation platform created based on the flight data of a Research Aircraft Flight Simulator (RAFS) manufactured by CAE Inc. which was accredited by the Federal Administration Authority (FAA) with the highest level of certification (Level-D). This control system is a promising technique for enhancing the performance and safety of the aircraft. This methodology utilizes a recurrent neural network to learn from a reference model and generate adaptive flight control commands that account for unpredictable changes in flight conditions. The Recurrent Neural Network is used to approximate the longitudinal dynamics of the aircraft while the neural weights are updated using pre-defined adaptation laws. The simulation results demonstrate that the proposed methodology outperforms the PID control approach while satisfying the robustness and adaptability for controlling the pitch rate of the aircraft under different flight conditions.
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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) | 0.000 | 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 it