Design of a Type Two Fuzzy-based system to Control the Pitch Rate of the Cessna Citation X
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3802.vid In this research, a novel control methodology developed at the Laboratory of Applied Research in Active Controls, Avionics and AeroServoElasticity (LARCASE) based on a Type Two Adaptive Fuzzy Sliding Mode control system to control the pitch rate of the Cessna Citation X aircraft is presented. For this purpose, a simulation platform developed at LARCASE was used to simulate the non-linear behavior of the Cessna Citation X aircraft. This platform was designed using flight data obtained from a Research Aircraft Flight Simulator (RAFS) manufactured by CAE Inc. and which has a Level D qualification for its flight dynamics and propulsion model. According to the Federal Aviation Administration (FAA), this is the highest level of qualification for flight simulators. This study aims to design a controller that can satisfy pitch rate tracking performance. Hence, Type Two Adaptive Fuzzy Sliding Mode Control (T2AFSMC) is suggested for this application to combine the robustness of Sliding Mode Control (SMC) with the flexibility of Type Two Fuzzy Logic Control (T2FLC). Traditional SMC has some limitations in dealing with uncertainties, nonlinearities, and disturbances in complex systems. Therefore, a Type Two Fuzzy Logic System is used to approximate the unknown dynamics of the aircraft while the sliding mode controller drives the aircraft to its desired state. In addition, some adaptation laws are employed to tune the parameters of the Type Two Fuzzy System during the simulation period. Finally, the simulation outputs demonstrate the effectiveness of the T2AFSMC in achieving good tracking performance and robustness in controlling the pitch rate of the Cessna Citation X aircraft as a complex system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".