Flight Control Modeling and Integration from a Real-Time Systems Simulator to a Flight Training Device
Bibliographic record
Abstract
Simulation is an important tool in control system design. Real-time simulation of flight controllers for the GARTEUR designed RCAM challenge was previously conducted on the University of Toronto Institute for Aerospace Studies real-time systems simulator (RTSS). As a next step to the controller design and simulation problem, it was desired to test a controller in the flight training device (FTD), which presents a more complex and realistic aircraft model, as well as offering a different visual perspective. The FTD runs on a commercial simulation software package called FLSIM. This project consisted of transferring two controllers from the RTSS to a FLSIM module. In order to duplicate the RCAM landing approach, the trajectory generator was also transferred. All other systems, such as flight dynamics and control actuators, were modeled by FLSIM. Through this exercise, a procedure for transferring models from the RTSS to the FTD was developed. Furthermore, it was found that controllers developed in the RTSS function in the FTD environment, but require tuning to achieve optimal results due to the more complex operational environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".