Distributed Simultaneous Fault Estimation and Cluster Consensus Control of Small Satellites
Bibliographic record
Abstract
In this study, the distributed fault estimation and control of clusters of satellites with heterogeneous nonlinear dynamics is investigated to determine the magnitude and shape of the unbounded faults in different clusters’ agents while reaching the cluster consensus in the company of faults and external disturbances. In the proposed fault estimation method, an augmented system is constructed for each satellite based on its communication topology to estimate the states and faults of that satellite and all its neighbors. Additionally, the observer used in this approach is an unknown input observer to decouple and minimize the effect of external disturbances on error dynamics. The coefficient matrices are calculated using linear matrix inequalities to reach consensus and estimate the fault simultaneously. Furthermore, to have a robust fault estimation, the${\mathbf{H}}_\infty $performance level${\boldsymbol{\gamma}}$is selected as an adjustable parameter to improve the state and fault estimation performance. The simulation results are shown for two clusters, including seven small satellites with different disturbances and Lipschitz-based nonlinearities. The results show that by using the proposed approach, the observer implemented in one cluster can estimate the states and faults of satellites in other clusters, minimizing the computational load for large clusters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".