Cooperative Optimal Synchronization of Networked Uncertain Nonlinear Euler–Lagrange Heterogeneous Multi-Agent Systems With Switching Topologies
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Bibliographic record
Abstract
This paper is concerned with design of distributed optimal synchronization control strategies for a class of networked nonlinear heterogeneous multi-agent (HMA) systems whose dynamics are governed by Euler–Lagrange (EL) equations. We employ optimal control techniques to design synchronization (consensus seeking) and set-point regulation controllers for HMA systems through optimization of individual cost functions. We introduce an analytical solution to the optimization problem and show that the developed optimal control laws can manage switchings in the communication network topology. Additionally, we propose two control strategies (namely, adaptive and robust) to modify and generalize the developed optimal control laws in presence of parametric uncertainties in the HMA systems. Simulation results for the attitude synchronization control of a network of eight spacecraft are presented to demonstrate the effectiveness and capabilities of our proposed 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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