<b>H∞</b>robust synchronisation of nonlinear multi-agent systems with sampled-data information
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Bibliographic record
Abstract
A distributed H∞ controller is presented for nonlinear multi-agent systems in this paper. The nonlinear dynamics of each agent are characterised by the Lipschitz condition. With the appearance of system uncertainty and external disturbance, a sampled-data feedback control protocol is carried out along the Lyapunov functional approach. Meanwhile, a state observer is incorporated to reinforce the capability of the proposed control strategy. It is demonstrated that the synchronisation of the networked nonlinear agents are essentially achieved with locally shared information. Remarkably, the system uncertainty and external disturbance are considered in the controller design and the influence caused by L2-bounded disturbance is minimised effectively. Furthermore, the control gain and observer gain derivation are equivalently transformed to a convex optimisation problem, which is solved by an iterative algorithm developed based on the sufficient conditions of system stability. The effectiveness of the proposed controller is verified by simulations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.006 | 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