Quantized-Observer-Based Event-Triggered Secure Consensus for NMASs Under DoS Attacks
Bibliographic record
Abstract
In order to study the safety control of linear cyber-physical systems, under constrained network communication burden, this article conducts research on the event-triggered cooperative stabilization problem of networked multiagent systems (NMASs) with unavailable states and denial-of-service (DoS) attacks. First, we construct a state observer to estimate unavailable internal system state. Second, a quantization control policy is proposed between the encoder and the decoder for reducing the data transmission. Third, we develop an event-triggered mechanism (ETM) with quantized state estimation to cut down the occupation of network communication bandwidth. To make up for the influence of DoS attacks launched between the observer and the controller channel, we establish an event-triggered hybrid controller based on quantized state observer to carry out secure consensus of NMASs, and deduce the secure consensus conditions via multiple Lyapunov functions. Moreover, the Zeno behavior is effectually eliminated due to the fact that each network node has a lower positive bound. In the end, the effectiveness of the developed safety control strategy via quantized observer is revealed through an example.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".