Dynamic Coupling and Control Response Effects on Spacecraft Handling Qualities During Docking
Bibliographic record
Abstract
NASA is developing a newgeneration of spacecraft to replace the Space Shuttle and return astronauts to themoon. These spacecraft will have a manual control capability for several mission tasks, and the ease and precision with which pilots can execute these tasks will have an important effect on mission risk and training costs. A simulation evaluated the handling qualities of a generic space vehicle based on dynamics similar to one of these spacecraft, NASA’s Crew Exploration Vehicle, during the last segment of the docking task with a space station. This handling qualities evaluation looked at fourdifferent translational control systems, called response types, thatmappilot inputs to thruster firings in a way that gives predictable and useful vehicle responses. These response types were flownwith three levels of translation-into-rotation dynamic coupling arising from a longitudinal offset between the reaction control system thrusters and the vehicle’s center of mass. The results indicate that greater translation-into-rotation coupling is strongly correlated with degraded handling qualities, but that different response types do not have a major effect on pilot workload, final docking performance, or handling qualities. Nomenclature FN, FE, FD = vehicle-carrying frame components of nongravitational force acting in the north direction, in the east direction, and along the
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".