Distributed Formation Recovery Control of Heterogeneous Multiagent Euler–Lagrange Systems Subject to Network Switching and Diagnostic Imperfections
Why this work is in the frame
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Bibliographic record
Abstract
This brief is concerned with the design of distributed formation recovery control laws for nonlinear heterogeneous multiagent Euler-Lagrange (EL) systems that are simultaneously subject to: 1) diagnostic information imperfections and unreliabilities; 2) parametric uncertainties and external disturbances; and 3) random switching of communication network topologies. The proposed recovery control techniques ensure both state synchronization and set-point tracking of a team of multiagent systems, while the agents have access only to local information. Our results are obtained for both fixed and switching communication network topologies. Distributed control recovery solutions for a general class of nonlinear multiagent EL systems have not been investigated earlier in the literature under the above simultaneous three realistic scenarios. The simulation results for the attitude control of a network of eight spacecraft tasked in a formation flying mission subject to communication topology switching demonstrate the effectiveness and capabilities of our proposed distributed recovery control strategies.
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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) | 0.001 | 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