Event-Triggered Adaptive Optimal Fast Terminal Sliding Mode Control Under Denial-of-Service Attacks
Bibliographic record
Abstract
This article develops an event-based adaptive optimal fast terminal sliding mode control (AOFTSMC) under malicious denial-of-service (DoS) attacks. It is supposed that the transmitted measurement signals are ruined by attackers randomly. A key issue is how to design the controller parameters to keep the desirable performance of the closed-loop system under DoS attacks which are characterized by their frequencies and durations. To this end, the event-based AOFTSMC is proposed first to increase robustness against the attack and reduce the computational load. Then, an explicit effect of the duration and frequency of DoS attacks on the stability of the closed-loop systems under the presented controller is analyzed. Moreover, the scheduling of controller updating times is determined. This leads to derive the maximum bandwidth of the cyber layer which is required to guarantee the stability of the closed-loop system. Then, the designer can outline suitable controller parameters in different situations in the presence of uncertainties and DoS attacks. Finally, numerical simulation results illustrate the validation and effectiveness of the proposed methodology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".