Use of Pitch and Heave Motion Cues in a Pitch Control Task
Bibliographic record
Abstract
During pitch rotation of the aircraft, a pilot, seated in front of the aircraft center of gravity, is subjected to rotational pitch and vertical heave motion. The heave motion is a combination of the vertical motion of the aircraft center of gravity and the heave motion as a result of the pitch rotation. In a pitch tracking task, all of these cues could potentially have a positive effect on performance and control behavior, as they are all related to the aircraft pitch attitude. To improve the tuning of flight simulator motion filters, a better understanding of how these motion components are used by the pilot is required. First, the optimal use of the different motion components was evaluated using an optimal control analysis. Next, an aircraft pitch attitude control experiment was performed in the SIMONA Research Simulator, investigating the effects of pitch rotation, pitch heave, and center of gravity heave on pilot control behavior. Pilot performance significantly improved with pitch motion, with an increased crossover frequency for the disturbance open loop. The increase in performance was a result of an increased visual gain and a reduction in visual lead, allowed for by the addition of pitch motion. Pitch heave motion showed similar but smaller effects. The center of gravity heave motion, although taking up most of the simulator motion space, was found to have no significant effects on performance and control behavior.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".