Nussbaum-based fractional-order sliding-mode fault-tolerant cooperative control of multiple UAVs with event-triggered mechanism
Bibliographic record
Abstract
To solve the problem of in-flight actuator faults and parameter uncertainties for multiple Unmanned Aerial Vehicles (UAVs), and reduce the communication and computational resource consumption of multiple UAVs, a Fraction-Order (FO) sliding-mode Fault-Tolerant Cooperative Control (FTCC) strategy is proposed for multiple UAVs based on Event-Triggered Communication Mechanism (ET-COM-M) and Event-Triggered Control Mechanism (ET-CON-M). First, by considering the limited communication bandwidth of multiple UAVs in formation, an ET-COM-M is designed to significantly reduce communication times. Then, a distributed observer is skillfully constructed to estimate the reference signals for follower UAVs. Moreover, the adaptive strategy is incorporated into the Radial Basis Function Neural Network (RBFNN) to learn the lumped unknown terms for handling bias actuator faults and parameter uncertainties. Besides, the Nussbaum method is used to deal with the loss-of-effectiveness faults. To further achieve the refined control performance against faults, FO calculus is artfully integrated into the sliding-mode control protocol with ET-CON-M. Finally, Zeno behavior is excluded by rigorous theoretical analysis and Lyapunov stability is proved to show the effectiveness of the designed FTCC strategy. Simulation results show that the designed FTCC strategy with Event-Triggered Mechanism (ETM) can guarantee the safety of multiple UAVs and simultaneously reduce the communication and control frequencies, making the developed control scheme applicable in engineering.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".