Distributed Adaptive Fault-Tolerant Cooperative Control for Multi-UAVs Against Actuator and Sensor Faults
Bibliographic record
Abstract
In this paper, a distributed adaptive fault-tolerant cooperative control (FTCC) scheme is developed for flying multiple unmanned aerial vehicles (UAVs) with consideration of actuator and sensor faults. The communication network is an undirected, fixed topology and only a subset of UAVs has access to the common reference. By using a sliding-mode observer, the common reference is estimated by each UAV. The lumped uncertainties including external disturbances, actuator and sensor faults are estimated by an adaptive mechanism. On the basis of the estimated reference and lumped uncertainties, dynamic surface control technique is utilized to eliminate the computational burden inherent in the traditional backstepping control architecture. The highlight is that external disturbances, actuator and sensor faults are considered in the distributed control scheme for multi-UAVs simultaneously. By using graph theory and Lyapunov-based method, it is proved that all signals in the closed-loop system are bounded. Furthermore, simulation results are exhibited to demonstrate the effectiveness of the proposed control 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".