Practical finite-time consensus of multi-agent systems with unknown nonlinear dynamics and the asymmetric input dead zone
Bibliographic record
Abstract
This paper investigates the practical finite-time consensus problem for heterogeneous multi-agent systems with unknown nonlinear dynamics, the asymmetric input dead zone, and external disturbances under directed topology. The model of heterogeneous multi-agent systems is composed of first-order and second-order dynamics. First, we show that under the proposed protocol, the sliding mode surface converges to a compact set in finite time. Then, we prove that the position errors and the velocity errors (for second-order agents) between any two agents reach a small desired neighborhood of the origin in finite time. In this approach, adaptive neural networks are employed to compensate for the nonlinear dynamics of agents. By applying sliding mode control, the external disturbances and the imperfect approximation of neural networks are rejected. The approach of the adaptive compensator plus dead zone is applied to overcome the asymmetric input dead zone. Besides, our proposed protocol is fully distributed, which means that the global graph information is not required beforehand by adaptive control gains. The effectiveness of our proposed protocol is finally validated through numerical simulations.
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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 | 0.002 | 0.004 |
| 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 | 0.002 |
| 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".