Finite-time consensus of heterogeneous unknown nonlinear multi-agent systems with external disturbances via event-triggered control
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Bibliographic record
Abstract
This article investigates the practical finite-time consensus for a class of heterogeneous multi-agent systems composed of first-order and second-order agents with heterogeneous unknown nonlinear dynamics and external disturbances in an undirected communication topology. To reduce the system updates, we propose an event-triggered approach. By defining auxiliary states, an adaptive distributed event-triggered control is designed to achieve practical finite-time consensus. Unknown nonlinear dynamics for each agent are estimated using radial basis function neural network. The stability of the overall closed-loop system is studied through the Lyapunov criterion. It is proven that by applying the proposed control scheme, the local neighbor position error and the velocity error between any two agents converge to a small region in finite time. Furthermore, it is shown that the Zeno behavior is ruled out. Finally, applicability and effectiveness of the proposed control scheme is verified and validated by two examples.
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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 | 0.000 |
| 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