Secure Consensus Control of Multiagent Cyber-Physical Systems With Uncertain Nonlinear Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Achieving consensus over a class of multiagent systems (MASs) under cyberattacks is studied in this article. The existing literature on secure consensus control of under-attack MASs is based on linear properties of agents models, whereas in practice, linearization may not be feasible in the presence of model uncertainties. Based on this motivation, the main contribution of this article is secure consensus control of MASs in the presences of uncertain nonlinearities in agents models. An MAS is considered consisting of a set of normal agents and a set of attacked malicious agents. A criterion is developed under which each normal agent at each time instant selects safer interaction links to avoid divergence from a consensus/agreement state in the presence of the unknown malicious agents. Accordingly, a network of nonlinear robust controllers is proposed such that under the selection criterion and in the presence of uncertain nonlinearities in the agents models, consensus among the normal agents is guaranteed. Numerical examples validate the accuracy of the proposed consensus control scheme.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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