Intelligent autolanding controller design using neural networks and fuzzy logic
Bibliographic record
Abstract
Designing an intelligent controller for landing phase of a jet transport aircraft in presence of different wind patterns, in order to expand the flight safety envelope has been considered. There are some dangerous conditions like gusts and downbursts, which may occur rarely in service life of aircraft, though aircraft must be tested for these dangerous conditions. Then it is desired to design a controller that not only acts well in usual conditions but also has an acceptable performance in those hazardous conditions. Four different types of controllers have been designed named PID, neuro, hybrid neuro-PID and anfis-PID (adaptive network-based fuzzy inference system) controllers. Simulation results show that the anfis-PID, which its inner loop is PID and outer loop is anfis, satisfies desired conditions in presence of very strong gust. However, the performance of neuro-PID is also acceptable. To evaluate the performance of controllers two level of performance have been defined named level I (desired) and level II (acceptable). Also, in comparison with JFK airport gusts two strong wind patterns named strong and very strong winds have been applied.
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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) | 0.001 | 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 | 0.000 |
| 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".