Spacecraft formation flying with connectivity maintenance under directed spanning tree
Bibliographic record
Abstract
This study explores control strategies for spacecraft formation flying under directed communication constraints with limited transmission ranges. Traditional approaches often face challenges in simultaneously maintaining persistent connectivity and achieving formation convergence in directional networks. To overcome these issues, a hierarchical control framework is proposed, combining edge-consensus algorithms with adaptive artificial potential functions (APFs). By formulating an edge-centric dynamic model that explicitly captures directional interactions, the method addresses the limitations inherent in conventional node-based approaches. A novel APF design introduces adaptive gradient terms, enabling real-time trade-offs between collision avoidance and connectivity preservation without imposing restrictive assumptions. The framework integrates feedback linearization with backstepping control, effectively mitigating external disturbances while achieving velocity synchronization. Simulation results confirm asymptotic convergence of the formation, with position root-mean-square error maintained below 0.15 m and velocity errors under 0.05 m/s. Furthermore, connectivity is preserved within 10 m of the upper safe limit under J 2 perturbations. Overall, the proposed methodology offers a scalable and reliable solution for multi-agent coordination in constrained orbital settings.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".