Neural network adaptive controller with approximate dynamic inversion for pitch control of the Cessna Citation X
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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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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)
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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 it