Secure Dynamic Event-Triggered Formation Tracking Control for Multiagent Systems
Bibliographic record
Abstract
This article studies the secure formation tracking problems for leader–follower multiagent systems (MASs) under limited resources, external disturbances, measurement noise, and random deception attacks, where a Bernoulli process is used to model random deception attacks occurring in the communication channels. A distributed sliding-mode observer (SMO) is proposed to estimate the inaccurate states of MASs, while handling the external perturbations. Then, a distributed dynamic triggering scheme is proposed based on the SMO states such that the interevent interval can be adjusted dynamically, and thus reduce the unnecessary resource consumption. By means of the SMO states and the event-triggered scheme, a distributed secure control protocol is proposed to realize the formation for MASs in the presence of one-to-all random deception attacks. Furthermore, it is extended to one-to-one random deception attack scenario. By employing the Lyapunov function and the linear matrix inequalities method, some sufficient conditions are provided to guarantee the bounded formation for MASs under a directed graph and both one-to-one and one-to-all attack scenarios. Experiments using multiple quadrotors are conducted to verify the developed formation 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 | 0.001 |
| 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".