Application of Type One Adaptive Fuzzy Sliding Mode Control System for the Longitudinal Motion of the Cessna Citation X
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3801.vid This paper presents a new approach developed at the Laboratory of Applied Research in Active Controls, Avionics and AeroServoElasticity (LARCASE) to control the longitudinal motion of the Cessna Citation X aircraft during cruise using a Type One Adaptive Fuzzy Sliding Mode control system. To achieve this, the nonlinear model of the aircraft was generated using the simulation platform developed at LARCASE, which was validated with flight data obtained from a Level D Research Aircraft Flight Simulator (RAFS) manufactured by CAE Inc. The term Level-D refers to the highest certification level of an aircraft's flight dynamics that can be issued by the Federal Administration of Aviation (FAA, AC 120-40B). A Type One Fuzzy Logic system was used to approximate the uncertainties and nonlinearities that may hinder the aircraft's performance, while the sliding mode control technique was chosen to ensure that the pitch rate can track the desired reference value. To take into account the variations in the aircraft's parameters and dynamics in different flight conditions, an adaptive control approach was adopted for the proposed controller to update the parameters to their optimal values over time. Based on the simulation results, the proposed controller robustly maintained the pitch rate of the aircraft at the reference value in the presence of variation in aircraft dynamics, while providing smooth elevator deflection to prevent any mechanical damage to the actuators in various flight conditions.
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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 | 0.000 |
| 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) | 0.002 | 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".