A worst-case approach to safety and reference tracking for cyber-physical systems under network attacks
Bibliographic record
Abstract
In this technical note, the safety and reference tracking control problems for Cyber-Physical Systems (CPSs) equipped with authenticated communication channels are addressed. In this class of CPSs, network attacks can break the feedback loop at two different points for an arbitrarily long period. In this scenario, we design a novel control architecture that, by taking a worst-case approach, aims to preserve the safety of the systems while minimizing, whenever possible, the tracking performance degradation. On the plant side, a local safety controller is designed to take care of attacks on the actuation channel. In particular, given a finite number of pre-determined admissible safe equilibrium points, this unit exploits a Voronoi partition of the state space and a family of dual-model set-theoretic model predictive controllers to safely confine, in a finite number of steps, the system into the closest robust control invariant region. On the other hand, on the controller side, the reference tracking controller operations are enhanced with an add-on module in charge of dealing with attack occurrences on the measurement channel. Specifically, by leveraging the Voronoi partition used on the plant's side and reachability arguments, the objective of this unit is to reduce the performance loss by allowing a supervised system evolution until the best outcome in terms of tracking is achieved. The obtained theoretical results are proved and the solution's effectiveness is shown through a simulation 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".