Neural network adaptive controller with approximate dynamic inversion for pitch control of the Cessna Citation X
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3798.vid This paper presents a technique for designing an adaptive nonlinear controller for the pitch rate of the Cessna Citation X business jet aircraft. The proposed control algorithm includes 3 major control elements, namely a baseline Proportional-Integral-Derivative linear controller, an approximate dynamic inversion, and an adaptive neural network. The dynamic inversion is performed online using estimates of the control and state matrix, determined from the Recursive Least Square method. The simulation results showed that the proposed control algorithm was perfectly capable of tracking a given reference signal defining a desired dynamic for the pitch control. The flight controller was tested on 20 different flight conditions across the flight envelope of the Cessna Citation X, and demonstrated good adaptation performance. The gain of the Proportional-Integral-Derivative controller remained constant for all flight conditions, while the adaptation was achieved by the neural network and the dynamic inversion. The control algorithm was then tested with different parameters, and very good performance was obtained for given reference signals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".