Event-triggered resilient consensus control of multiple unmanned systems against periodic DoS attacks based on state predictor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper develops an event-triggered resilient consensus control method for the nonlinear multiple unmanned systems with a data-based autoregressive integrated moving average (ARIMA) agent state prediction mechanism against periodic denial-of-service (DoS) attacks. The state predictor is used to predict the state of neighbor agents during periodic DoS attacks and maintain consistent control of multiple unmanned systems under DoS attacks. Considering the existing prediction error between the actual state and the predicted state, the estimated error is regarded as the uncertainty system disturbance, which is dealt with by the designed disturbance observer. The estimated result is used in the design of the consistent controller to compensate for the system uncertainty error term. Furthermore, this paper investigates dynamic event-triggered consensus controllers to improve resilience and consensus under periodic DoS attacks and reduce the frequency of actuator output changes. It is proved that the Zeno behavior can be excluded. Finally, the resilience and consensus capability of the proposed controller and the superiority of introducing a state predictor are demonstrated through numerical simulations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it