A Novel Particle Swarm Optimization Based Fuzzy Super-Twisting Sliding Mode Control System for the Lateral Motion of Cessna Citation X
Bibliographic record
Abstract
This paper presents a new Artificial Intelligence (AI) controller developed at the Laboratory of Applied Research in Active Controls, Avionics and AeroServoElasticity (LARCASE) based on a Type Two Adaptive Fuzzy Super-Twisting Sliding Mode Control system (T2AFSTSMC) enhanced by the Particle Swarm Optimization (PSO) method for the lateral motion of the Cessna Citation X (CCX) aircraft. For this purpose, a simulation platform developed at the LARCASE was used to simulate the dynamics of the CCX. This platform was designed using flight data obtained from a Research Aircraft Flight Simulator (RAFS) manufactured by CAE Inc., which has a Level-D qualification showing the highest precision level of flight simulators according to the FAA. This study aims to design two control systems: one for satisfying the roll rate tracking performance, and another one to stabilize the yaw rate using a PID controller. This T2AFSTSMC combines the robustness of the Sliding Mode Control and the flexibility of Type Two Adaptive Fuzzy Logic system (T2AFLS). The T2AFLS serves as an approximator for the aircraft unknown dynamics. The PSO was employed to fine-tune the controller parameters. Simulation results demonstrated the effectiveness of the controller in handling the uncertainties and tracking the given roll rate reference during cruise.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".