Resilient Model Predictive Control of Cyber–Physical Systems Under DoS Attacks
Bibliographic record
Abstract
This article presents a resilient model predictive control (MPC) framework to attenuate adverse effects of denial-of-service (DoS) attacks for cyber-physical systems (CPSs), where the system dynamics is modeled by a linear time-invariant system. A DoS attacker targets at blocking the controller to actuator (C-A) communication channel by launching adversarial jamming signals. We show that, in order to guarantee exponential stability of the closed-loop system, several conditions for resilient MPC should be satisfied. And these established conditions are explicitly related to the duration of DoS attacks and MPC parameters such as the prediction horizon and the terminal constraint. Two key techniques, including the μ-step positively invariant set and the modified initial feasible set are exploited for achieving exponential stability in the presence of DoS attacks. Moreover, the maximum allowable duration of the DoS attacker is also obtained by using the μ-step positively invariant set. Finally, the effectiveness of the proposed MPC algorithm is verified by simulated studies and comparisons.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".