Model Predictive Controller with Adaptive Neural Networks and Online State Estimation for Pitch Rate Control of the Cessna Citation X
Bibliographic record
Abstract
This research 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 components, namely a Model Predictive Controller (MPC), an online state estimation, and an adaptive neural network controller. The estimation of the aircraft local dynamics is performed using Recursive Least Square (RLS) method. The RLS algorithm consists of an adaptive filter suited for state approximation which only requires states and control increments. The MPC controller consists of an optimization of the control trajectory over a given control, and predicted horizons. A constrained nonlinear optimization is fulfilled to solve the optimization problem, for which a quadratic cost function with respect to the control trajectory is minimized. Finally, an adaptive neural network is added, which compensates the errors in the state estimations and improves the overall performance of the controller. The neural network component consists of a single hidden layer feedforward neural network, for which the hyperparameters are updated online using backpropagation principle on the weights of the input and output layers. The simulation results showed that the proposed control algorithm was perfectly capable of tracking a given reference signal defining a desired flight dynamic for the pitch rate control. The flight controller was tested on 63 different flight conditions across the flight envelope of the Cessna Citation X, and demonstrated good adaptation performance. The control algorithm was then tested with different parameters of the controller elements, and very good performance was obtained for given reference signals on the pitch rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".