Modeling the Longitudinal Dynamics of the Cessna Citation X using Neural Network Methodology
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-2191.vid This paper presents a methodology developed at the Laboratory of Applied Research in Active Controls, Avionics and AeroServoElasticity (LARCASE) to model the linearized longitudinal dynamics of the Cessna Citation X business jet using artificial neural networks. For this purpose, a simulation platform developed at LARCASE was used to generate the aircraft longitudinal state space matrices {A, B} for a wide range of operating conditions. This simulation platform was developed and validated from data obtained from a Level D Research Aircraft Flight Simulator (RAFS) designed and manufactured by CAE Inc. According to the Federal Administration Aviation (FAA, AC 120-40B), the level D is the highest certification level for the flight dynamics of an aircraft. The data collected from the simulation platform was then restructured into a comprehensive database for the neural network training process. In this study, the structure of the neural network was determined by performing several analyses in order to find the optimal number of layers and neurons, as well as the combination of activation and learning functions, that provide the best prediction results. The validation of the neural network model was performed in two steps. First, analysis was performed by comparing the longitudinal matrix {A, B} predicted by the neural network with the matrix obtained from the simulation platform. Then, a second analysis was performed by comparing the aircraft dynamics parameters (pitch angle, normal acceleration and time variations) for two modes - the short period and the phugoid obtained using neural network versus the simulation platform. The results showed that the proposed model provides very accurate predictions of longitudinal dynamics.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".