Active fault-tolerant control system vs Model Reference Adaptive Control system: Performance comparison on flight systems
Bibliographic record
Abstract
Modern avionic systems are still designed based only on the sensors' measurements, without considering actuator fault scenarios. Acting on several control surfaces, actuators are considered as critical components and therefore, failed actuators can lead to undesirable consequences as degrade performances or even loss of stability during flight. To handle this problem, actuator hardware redundancy has been used for many years ago. However, this technique is expensive and involves increased weight and space. Therefore, new control approaches, based on the online actuator fault diagnosis and fault effect compensation, are developed to deal with these disadvantages. These approaches are known in the literature as fault-tolerant flight control systems. In this paper, a fault-tolerant flight control system is designed and applied to the linear lateral motion of the Boeing 747. Numerical simulations are performed to compare its performance and effectiveness with existing approaches. FlightGear software simulator is used to show the behavior of the aircraft on a graphical user interface.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".