Distributed H<inf>&#x221E;</inf> optimal control of networked uncertain nonlinear Euler-Lagrange systems with switching communication network topologies
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Bibliographic record
Abstract
This paper is concerned with the design of distributed state synchronization and trajectory tracking control laws for nonlinear Euler-Lagrange (EL) systems in presence of parameter uncertainty and external disturbances with fixed and switching communication network topologies. Specifically, H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> optimal control techniques are employed to formally design controllers which address the state synchronization and trajectory tracking of a team of multi-agent nonlinear EL systems while the agents have access to only local information. It is shown that the state synchronization (or consensus) protocol and trajectory tracking controllers can be formally derived by employing our proposed analysis. In addition, we formally show that our proposed distributed state synchronization and tracking control algorithms for EL systems is input-to-state stable (ISS) where the input is taken as parameter uncertainty as well as external disturbances. Our results are obtained for both fixed and switching communication network topologies. Simulation results for attitude control of a network of spacecraft demonstrate the effectiveness and capabilities of our proposed distributed control algorithms.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 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 it