Robust Distributed MPC for Constrained Multi -Agent Systems against DoS Attacks
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Bibliographic record
Abstract
The paper investigates the formation stabilization problem of nonlinear multi-agent systems (MASs). Each agent of the MASs studied in this paper is subject to external disturbances and there also exist denial-of-service (DoS) attacks affecting the communication channels among agents. To tackle these issues, a robust distributed model predictive control (MPC) scheme is proposed, in which a novel strategy is developed to compensate for the missing state information from neighbors induced by the DoS attacks. As a result, the effect of DoS attacks can be mitigated by using the proposed control strategy. In the meantime, the influence of additive disturbances is alleviated by designing the robustness constraints in the proposed distributed MPC. Finally, the effectiveness of proposed scheme is verified by the numerical simulation result.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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