Tau Guidance in Boundary-Avoidance Tracking - New Perspectives on Pilot-Induced Oscillations
Bibliographic record
Abstract
Tau theory, introduced to the flight control discipline as a model for natural guidance, is shown to provide an approach to predicting a class of adverse aircraft-pilot couplings described as boundary-avoidance tracking events and pilot-induced oscillations. These have previously been modeled a posterior as discrete events using timedependent feedback gains. Drawing on the prospective nature of the time-to-contact variable optical tau �, a new method is proposed for modeling such phenomenon and also for determining the critical incipience for this class of aircraft-pilot coupling. In the present study, the approach has been applied to tau guidance in a rotorcraft trajectory tracking maneuver, to predict the conditions under which aircraft-pilot couplings may occur. In addition, a strong correlation between motion and control activity and the derivatives of tau adds substance to the hypothesis that the pilot’s perceptual system works directly with invariants in the optical flow during visual guidance. Results from flight simulation tests conducted at the University of Liverpool and complementary flight tests carried out with the National Research Council (Canada) advanced systems research aircraft in-flight simulator support the tau control hypothesis. The theory suggests ways that pilots could be alerted to the impending threat of such adverse aircraftpilot couplings
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".