Robust and Resilient Distributed MPC for Cyber-Physical Systems Against DoS Attacks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, considering the ubiquitously existing cyber attacks in cyber-physical systems (CPSs), we present a robust and resilient distributed model predictive control (MPC) strategy for CPSs with multi-agent architecture under denial-of-service (DoS) attacks to achieve the goal of cooperative regulation with all agents' states being regulated to their equilibrium. Each agent in the CPSs is subject to external disturbances, and the communication channels among agents might be affected by randomly occurring DoS attacks. To tackle these issues, firstly, a novel robustness constraint is designed to handle the uncertainties in the MPC algorithm. By adding this constraint, the state of the nominal system can be confined in a shrinking and tighter range compared to the classical MPC approach, thus resulting in enhanced robustness against uncertainties. Furthermore, a lengthened sequence transmission strategy is proposed to mitigate the effect of the lack of information in the communication channels induced by DoS attacks. At each time instant, the controller of each agent utilizes the predicted state information to compensate for the transmission block-out from one agent to another. Moreover, recursive feasibility for the control framework and the closed-loop stability for the overall system are guaranteed by theoretical analysis. Finally, simulation and comparison studies demonstrate the effectiveness of the proposed robust and resilient distributed MPC strategy.
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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) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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