Design of a Nonlinear Adaptive Fuzzy Logic System for Cessna Citation X Speed Control
Bibliographic record
Abstract
This paper presents the design of a new Adaptive Fuzzy Logic System for controlling the speed of the Cessna Citation X aircraft. The suggested methodology is proposed to satisfy both the tracking performance of the aircraft speed with a given reference signal, and to stabilize it in the presence of uncertainties existing in the aircraft model. The nonlinear aircraft model was generated using a simulation platform developed by previous researchers at LARCASE for the Cessna Citation X aircraft. This platform was designed based on real flight data from a Level-D Research Aircraft Flight Simulator designed and manufactured by CAE. The term Level-D denotes the highest rate of certification that is issued by the FAA for flight simulators. In this research, two fuzzy logic systems are considered to approximate the aircraft nonlinearities. Furthermore, these techniques include adaptation laws, whose purpose is to adjust the approximated functions during the simulation and for different flight conditions. A nonlinear sliding mode control system technique was used to guarantee the stability and robustness of the aircraft speed controller. Ultimately, the results were validated for all flight conditions over the whole flight envelope of the Cessna Citation X aircraft during the cruise phase.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".