Flying Qualities Assessment for Nonlinear Adaptive Control Validation on the Cessna Citation X Longitudinal and Lateral Dynamics
Bibliographic record
Abstract
This paper discusses the validation of nonlinear adaptive controllers by assessing the flying qualities, specifically applied to the flight dynamics of the Cessna Citation X. Two distinct nonlinear controllers were designed for low-level control of longitudinal and lateral dynamics, respectively. Both control algorithms include a fixed Proportional-Derivative-Integral baseline controller, a Nonlinear Dynamic Inversion controller using Recursive Least Square estimation, and an adaptive Neural Network controller. The longitudinal control consists of controlling the aircraft pitch rate, while the lateral control task consists of controlling the aircraft roll rate and ensuring the stabilization of the yaw rate. The flying qualities were taken from MIL-STD 1797A for which Level 1 requirements were met for short period, roll and Dutch-roll dynamics. The resulting controllers consist of Model Reference Adaptive Controllers (MRAC), which means that specific reference signals were tracked with desired performances on the pitch and roll rates. The flight controllers were tested for 64 flight conditions in the cruise phase covering the overall flight envelope of the Cessna Citation X. Simulations demonstrated minimal tracking error, and then transfer function identification was fulfilled for each simulation to extract the transient performance characteristics. Results have shown that the aircraft flying qualities were lying within the Level 1 for both adaptive longitudinal and lateral controllers.
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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".