Spacecraft Orbit-Attitude Coupled Dynamics in Close Proximity to Small-Bodies
Bibliographic record
Abstract
This dissertation presents the development of high definition models and techniques that advance the analysis and control of spacecraft dynamics in close proximity to the surface of small-bodies. First, a high definition gravitational field model for asteroids is proposed, that considers the highly irregular shape of the asteroid, while also including a perturbing effect known as gravitational orbit-attitude coupling. This model uses two existing gravitational representations: the polyhedron model to capture the main gravitational terms with high resolution, and a pointmass model that captures the coupling phenomena. The attitude impact on the translational dynamics is then studied under this model, and the possibility of stabilizing the spacecraft about equilibria via the coupling effect is studied. This approach to control is beneficial for reducing propellant consumption during close proximity operations. Next, due to the low magnitude of the orbit-attitude coupling phenomena, an improved method for accurate numerical integration in astrodynamics applications is presented. Since the spacecraft attitude impacts the trajectory, and vice-versa, any change on one aspect of the dynamics during numerical integration will affect the other. Therefore, having an accurate and computationally fast method is paramount to understand long term dynamical behaviour close to asteroids. The proposed methods are based on geometric mechanics principles, are very fast for long term propagations, and allow integration of spacecraft dynamics with high accuracy in highly perturbed gravitational environments. Finally, the inverse problem is studied. This is, knowing the evolution of the spacecraft state vector in time, how can the gravitational field model parameters be obtained. Here, an estimation technique that considers non-homogeneous mass distribution is developed, exploiting the orbit-attitude coupling phenomena. By using the coupling effect as a complement to traditional estimation methods, additional information to estimate density distributions inside the asteroid can be obtained, and an accurate model of the asteroid can be then be constructed. The implications of the results obtained will allow the design of more accurate control laws, and maneuvers required for landing or hovering above the surface can be planned more efficiently, augmenting the capability of future missions to these small-bodies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".