Decentralized event-triggered resilient control for a class of networked control systems under denial-of-service attacks
Bibliographic record
Abstract
This paper studies the problem of resilient dynamic output-feedback control schemes for continuous-time networked control systems (NCSs). A decentralized hybrid strategy is utilized while network imperfections, external disturbances, and noise are considered. Moreover, during data transmission, a practical denial-of-service (DoS) jamming attack, which periodically disturbs the network channels, is considered. To preserve the network resources, a decentralized event-triggered mechanism is employed to transmit only the sampled signals that are required. It is assumed that outputs and control inputs of each subsystem are transmitted to the corresponding decentralized controllers and actuators, respectively, over the different individual channels based on independent triggering mechanisms. At first, the NCS is modeled as a decentralized hybrid system with exogenous disturbance and noise. Then, sufficient conditions that guarantee the [Formula: see text]-stability of the NCS in the presence of external disturbances and noise, which is resilient to periodic DoS attacks, are provided in terms of the linear matrix inequalities. Finally, sufficient conditions are derived to deal with the time-varying delays. It has been demonstrated that the proposed technique can be effectively applied to a well-known continuous stirred tank reactor (CSTR) as a benchmark 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) | 0.001 | 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 | 0.001 |
| 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".