Secure Consensus Control of Multiagent Cyber-Physical Systems With Uncertain Nonlinear Models
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".