Robust Pitch Control of the Cessna Citation X Using C-Star Architecture and Adaptive Neural Networks
Bibliographic record
Abstract
This paper presents a technique for designing a robust pitch controller using a C* control architecture, and implementing adaptive methods for the longitudinal control of the Cessna Citation X business jet aircraft. First, the design of the inner loop aims to control the pitch rate and the load factor, also known as the C-star parameter. A Proportional–Integral– Derivative (PID) controller is combined with a Neural Network Adaptive controller. The gains of the PID remain constant for all flight conditions, while the NN hyperparameters are updated online. A reference signal is imposed on the C-star command in order to meet the handling quality requirements for longitudinal modes. Second, an adaptive pitch controller is proposed using a second neural network. The overall control algorithm is designed for cruise control and tested for 63 cruise conditions across the Cessna Citation X flight envelope. Its robustness is tested against uncertainties, including wind gust, wind shear and Dryden turbulence model with various intensities. Results show that the proposed control algorithm is perfectly able to control the aircraft and follow a given model reference pitch signal.
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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".