Helicopter Handling Qualities: A study in pilot control compensation
Bibliographic record
Abstract
Abstract The research reported in this paper is aimed at the development of a metric to quantify and predict the extent of pilot control compensation required to fly a wide range of mission task elements. To do this, the utility of a range of time- and frequency-domain measures to examine pilot control activity whilst flying hover/low-speed and forward flight tasks are explored. The tasks were performed by two test pilots using both the National Research Council (Canada)’s Bell 412 Advanced Systems Research Aircraft and the University of Liverpool’s HELIFLIGHT-R simulator. Handling qualities ratings were awarded for each of the tasks and compared with a newly developed weighted adaptive control compensation metric based on discrete pilot inputs, showing good correlation. Moreover, in combination with a time-varying frequency-domain exposure, the proposed metric is shown to be useful for understanding the relationship between the pilot’s subjective assessment, measured control activity and task performance. By collating the results from the subjective and objective metrics for a range of different mission task elements, compensation boundaries are proposed to predict and verify the subjective assessments from the Cooper-Harper Handling Qualities Rating scale.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".