Flight control clearance of the Cessna Citation X using evolutionary algorithms
Bibliographic record
Abstract
In this paper, an Aircraft Research Flight Simulator equipped with Flight Dynamics Level D (highest level) was used to collect flight test data and develop new controller methodologies. The changes in the aircraft’s mass and center of gravity position are affected by the fuel burn, leading to uncertainties in the aircraft dynamics. A robust controller was designed and optimized using the H ∞ method and two different metaheuristic algorithms; in order to ensure acceptable flying qualities within the specified flight envelope despite the presence of uncertainties. The H ∞ weighting functions were optimized by using both the genetic algorithm, and the differential evolution algorithm. The differential evolution algorithm revealed high efficiency and gave excellent results in a short time with respect to the genetic algorithm. Good dynamic characteristics for the longitudinal and lateral stability control augmentation systems with a good level of flying qualities were achieved. The optimal controller was used on the Cessna Citation X aircraft linear model for several flight conditions that covered the whole aircraft’s flight envelope. The novelty of the new objective function used in this research is that it combined both time-domain performance criteria and frequency-domain robustness criterion, which led to good level aircraft flying qualities specifications. The use of this new objective function helps to reduce considerably the calculation time of both algorithms, and avoided the use of other computationally more complicated methods. The same fitness function was used in both evolutionary algorithms (differential evolution and genetic algorithm), then their results for the validation of the linear model in the flight points were compared. Finally, robustness analysis was performed to the nonlinear model by varying mass and gravity center position. New tools were developed to validate the results obtained for both linear and nonlinear aircraft models. It was concluded that very good performance of the business Cessna Citation X aircraft was achieved in this research.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".