Fault-Tolerant Adaptive Neural Control of Multi-UAVs Against Actuator Faults
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Bibliographic record
Abstract
This paper is concerned with the fault-tolerant cooperative control (FTCC) problem of multiple unmanned aerial vehicles (multi-UAVs) in the communication network. By exploiting neural network (NN) to approximate the nonlinear terms existing in the highly nonlinear multi-UAVs system, a distributed neural adaptive control scheme is proposed when only a subset of follower UAVs has access to the leader UAV's states. To solve the problem of “explosion of complexity” in traditional backstepping architecture and reduce the number of online updating parameters of NN, dynamic surface control (DSC) and minimal learning parameter techniques are employed to reduce the computational complexity. Furthermore, by combining graph theory and Lyapunov approach, it is proved that velocities and altitudes of all follower UAVs can track the velocity and altitude of the leader UAV. Finally, simulation results are presented to verify the effectiveness of the proposed control scheme.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it