Hybrid impulsive flocking control for multi-agent systems with fault tolerance
Why this work is in the frame
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Bibliographic record
Abstract
The problem of leader-following flocking control in multi-agent systems with complex dynamical networks using hybrid impulsive and pinning control techniques, along with fault tolerance, has been investigated. Specifically, these mechanisms efficiently reduce control resource consumption and transmission redundancy. Additionally, they address parametric uncertainties, actuator failure, and deception attacks to enhance overall network stability. Moreover, gyroscopic and braking forces are utilized for obstacle avoidance. By leveraging transmission topology structure and impulsive control theory, sufficient criteria are derived to allocate admissible error bounds for maintaining flocking stability. Finally, numerical simulations are conducted to validate the theoretical analysis.; more precisely, it results in a 19.46% decrease in control cost compared to mainstream continuous control structures, and the alternative collision avoidance component leads to a 20.88% reduction in average control distortion.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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