Resilient Formation Tracking of Spacecraft Swarm Against Actuation Attacks: A Distributed Lyapunov-Based Model Predictive Approach
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Bibliographic record
Abstract
This article studies the resilient formation tracking control problem for spacecraft swarm while considering actuation attacks and input saturation. A distributed Lyapunov-based model predictive controller (DLMPC) framework is designed for spacecraft swarm to track the target trajectory in a preset formation shape and achieve attitude consensus. To ensure formation safety, a collision avoidance term is introduced into the DLMPC framework. To guarantee the feasibility and stability of the DLMPC, we first construct the Lyapunov-based adaptive auxiliary controller and then use its stability to construct the stability constraint. The DLMPC inherits the characteristic of the Lyapunov-based adaptive auxiliary controller and employs online optimization to guarantee better formation tracking performance. As a novel framework for spacecraft formation control, the proposed DLMPC has the advantage of improving the formation tracking performance through persistently online optimization, especially, in adversarial dynamic environments. The simulation results validate the superiority and resilience of the DLMPC, and the proposed DLMPC framework shows improvement in formation tracking performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it