Cessna Citation X Ground Speed Control using Deep Reinforcement Learning
Bibliographic record
Abstract
Throughout this study, a novel approach was developed at the Laboratory of Applied Research in Active Control, Avionics, and AeroServoElasticity (LARCASE) to control the ground speed of an aircraft using Reinforcement Learning. For this purpose, a Reinforcement Learning controller using a Deep Deterministic Policy Gradient agent was developed. This study used a non-linear longitudinal ground dynamics model of the Cessna Citation X aircraft that was identified and validated using a level-D Research Aircraft Flight Simulator (RAFS). During the training process, the DDPG agent learned to control the aircraft ground speed by controlling the engine fan speed (N1P) and the brake. To validate the proposed model, several simulations were performed with different ground speed profiles to evaluate its performance under different acceleration and deceleration conditions. Overall, the results indicated that the controller maintained the desired reference speed, regardless of the acceleration or deceleration profiles.
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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) | 0.000 | 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 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".