Event-Based Secure Consensus Control for Multirobot Systems With Cooperative Localization Against DoS Attacks
Bibliographic record
Abstract
In this article, we investigate the secure consensus control problem for multirobot systems with event-triggered communication strategy under aperiodic energy-limited denial-of-service (DoS) attacks, where DoS attacks prevent the transmission of information between robots. Each robot is equipped with onboard sensors to estimate its position cooperatively by taking relative measurements and exchanging the local positioning information with other robots through the unreliable communication network. In the meantime, each robot determines its consensus control based on transmitted position estimates and steers the robot to the desired consensus position. Therefore, our goal is to design a secure control scheme for each robot based on cooperative localization with an event-triggered mechanism and obtain a sufficient condition for the upper bound of duration and the maximum number of attacks such that$N$robots can move to the desired secure consensus position in the presence of DoS attacks. Finally, simulation and experimental results are presented to show the effectiveness of obtained theoretical results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".