Resilient Fault-Tolerant Cooperative Control for Multiple Unmanned Aerial Vehicles Under DoS Attacks and Actuator Faults
Bibliographic record
Abstract
This paper investigates the cooperative control problem for multiple unmanned aerial vehicles (UAVs) against denialof-service (DoS) attacks and actuator faults. DoS attacks can cut off the partial communication link to block the information exchange of the position state. In addition, actuator faults will degrade the control effectiveness of UAVs related to the aileron, elevator, and rudder. To mitigate DoS attacks, a fixedtime link weight estimation mechanism is proposed to achieve topology weight estimation with fixed-time characteristics for mitigating the effect of DoS attacks. To further compensate for actuator faults, an adaptive fault parameter estimation method is proposed to identify the actuator gain and bias fault parameters, respectively. Moreover, by combining the link weight and fault parameters estimation methods, a resilient fault-tolerant cooperative control (RFTCC) method is presented to achieve the cooperative formation of multi-UAVs with outer-loop and innerloop models against DoS attacks and actuator faults. Numerical simulation results verify that the proposed method accurately estimates gain and bias fault parameters and ensures desired formation maintenance under DoS attacks and actuator faults.
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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 | 0.000 |
| 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) | 0.000 | 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".