Neural Network Adaptive Controller with Approximate Dynamic Inversion for the Cessna Citation X Lateral Control
Bibliographic record
Abstract
This work outlines a method for designing an adaptive nonlinear controller for the lateral control of the Cessna Citation X business aircraft. The objective of the proposed controller is to control the roll rate while also stabilizing the yaw rate by following a given reference signal. The overall controller is composed of a Proportional-Integral-Derivative (PID) linear controller, an approximate Dynamic Inversion (DI) algorithm, and an adaptive neural network (NN) component. Online estimations of the control and state matrices, which are obtained using the Recursive Least Squares approach, are used for dynamic inversion. The simulation outcomes show that the designed controller successfully follows the reference signal and is able to stabilize the yaw rate. The entire flight envelope of the Cessna Citation X was used to assess the controller's performance under 63 flight conditions in cruise phase. The overall controller showed strong adaptability, in the way that the DI and NN have delivered the required adaptability, while the PID controller's gain remained constant for all flight conditions. The controller was also validated for different parameters variation, and showed a very good overall performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".