Dynamic Feasibility of Space Environment Emulation using an Omnidirectional Drone
Bibliographic record
Abstract
Spacecraft control systems are crucial to a space mission making them an important focus of pre-launch verification activities. Currently, some of the passive dynamic emulation testbeds are parabolic flight tests, neutral buoyancy facilities, and air-bearing-based testbeds. Unlike passive methods, active dynamic emulation methods can actively provide the required force/torque to emulate the desired environment. One way to achieve active dynamic emulation is using a feed-forward scheme. However, active methods may destabilize the system. Therefore, investigating the stability of the active method using the feed-forward term is critical. Using a feed-forward scheme for emulating inertia (inertia shaping) has been studied for haptic devices by researchers. This paper introduces a drone-based feed-forward dynamic emulation method and investigates its feasibility for spacecraft dynamic emulation. First, we introduce a mathematical model of translational and rotational dynamic emulation and study the stability in the presence of sensor delays. Then, numerical simulations in MATLAB/Simulink are presented to demonstrate the feasibility of the dynamic emulation method. Lastly, we conclude that the proposed dynamic emulation method is feasible as long as emulated mass and inertia are less than two times of drone’s mass and inertia, respectively.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".