Distributed H ∞ Optimal Formation Recovery Control of Heterogeneous Euler-Lagrange Systems Subject to Network Switching and Diagnostic Imperfections ★ ★This publication was made possible by NPRP Grant No. 5-045-2-017 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
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Bibliographic record
Abstract
This paper is concerned with the design of distributed formation recovery control laws for nonlinear Euler-Lagrange (EL) systems in presence of parameter uncertainty, external disturbances, and diagnostic information imperfections with switching communication network topologies. Specifically, H∞optimal control techniques are employed to formally design recovery controllers to accomplish state synchronization and trajectory tracking of a team of multi-agent nonlinear heterogeneous EL systems while the agents have access to only local information. Our proposed distributed state synchronization and tracking recovery control algorithms are input-to-state stable (ISS) where the input is considered to be parameter uncertainty as well as external disturbances. Our results are obtained for both fixed and switching communication network topologies.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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