Resilient Finite-Time Consensus Tracking for Nonholonomic High-Order Chained-Form Systems Against DoS Attacks
Bibliographic record
Abstract
This article studies the resilient finite-time consensus tracking problem for high-order nonholonomic chained-form systems against denial-of-service (DoS) attacks. The first step is to develop a novel secure distributed observer for each follower in which the tangent hyperbolic function is used to accelerate the convergence speed of the observer by inducing a high-gain effect. The paralyzed-connectivity graphs resulting from DoS attacks are repaired to the initially connected graphs by integrating both acknowledgment-based attack detection techniques and the communication recovery process. In addition, it is demonstrated that the duration of DoS attacks directly affects the convergence time of the proposed scheme. Then, a fast finite-time backstepping control (FFTBC) algorithm is established for each follower to track the estimated leader's information, ensuring fast convergence performance regardless of whether the follower states are near or far from the equilibrium point. An approximation-based approach is also presented for reducing the conservatism of the upper estimate of the settling time. An evaluation of the proposed control algorithm under DoS attacks is conducted using a group of wheeled mobile robots.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".