Integrated Fault Estimation and Fault-Tolerant Tracking Control for Unmanned Surface Vessels Under Connectivity-Hybrid Cyber-Attacks
Bibliographic record
Abstract
This study aims to tackle the tracking control problem of multiple unmanned surface vessels (USVs). It considers the impact of connectivity-hybrid cyber-attacks in the networked level, and wave-induced disturbances, as well as severe and nonsevere unified modeling rudder angle faults in the physical level. To do this, the study establishes USV models, taking into account actuator fault and cyber-attack modeling. It then presents the augmented estimator-based decentralized fault estimation (FE) and leader-following consensus-based distributed fault-tolerant tracking control (FTTC) protocols. These are incorporated into an integrated structure that ensures the robust asymptotic convergence of estimation errors and excellent tracking performance of multi-USVs. Finally, the study derives criteria for an exponential tracking of composite faulty multi-USVs under cyber-attacks using dual-constraint restriction (attack frequency and excitation rate). Comparative simulations substantiate the advantage of the developed integrated FE and FTTC scheme.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".