Distributed adaptive fractional‐order fault‐tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study presents a distributed fault‐tolerant cooperative control (FTCC) strategy to achieve the attitude synchronisation tracking control of networked unmanned aerial vehicles (UAVs) in the presence of actuator faults and model uncertainties. By utilising the fuzzy neural networks (FNNs), the unknown non‐linear terms induced by actuator faults and model uncertainties are estimated as lumped uncertainties. A set of distributed sliding‐mode estimators (DSMEs) is then employed to estimate the leader UAV's attitudes for the follower UAVs via a distributed communication network. Based on the estimated knowledge from FNNs and DSMEs, a group of distributed FTCC laws is developed for all follower UAVs by using the fractional‐order calculus. It is proven that with the proposed control scheme, all follower UAVs can track the attitudes of the leader UAV and the tracking errors are uniformly ultimately bounded even when a portion of networked UAVs encounters multiple actuator faults. Comparative simulation results are presented to demonstrate the effectiveness of the proposed approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it