Adaptive Pose Control for Spacecraft Proximity Operations With Prescribed Performance Under Spatial Motion Constraints
Bibliographic record
Abstract
In this article, a novel pose (i.e., concurrent position-attitude) tracking control framework is proposed for spacecraft proximity operations with a freely tumbling target, employing the prescribed performance control (PPC) methodology. Especially, the whole operations involved are divided into two synchronously occurring maneuvers: relative position tracking and boresight pointing adjustment. For the former, a new relative translational dynamics is established to facilitate its problem formulation and solving, while, for the latter, the desired attitude is extracted to align the boresight of the pursuer's onboard vision sensor toward the target. Given this, a noncertainty-equivalence adaptive pose controller is designed based on the PPC design approach integrating a class of appointed-time performance functions. It is shown that the designed controller is able to achieve prescribed performance guarantees for the pose tracking errors and, meanwhile, guarantee asymptotic convergence of both the velocity and angular velocity errors, regardless of mass and inertia uncertainties. The salient feature of the proposed method is that, by judiciously imposing the performance specifications on the pose tracking errors, it can: 1) enable the pursuer to accomplish the proximity operations in a designer-appointed time and 2) ensure compliance with spatial motion constraints and avoid singularity of the attitude extraction algorithm. Finally, simulation results are presented to illustrate the effectiveness of the proposed method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".