Adaptive consensus for heterogeneous unknown nonlinear multi-agent systems with asymmetric input dead-zone: A finite-time approach
Bibliographic record
Abstract
This study deals with the adaptive finite-time consensus problem of heterogeneous multi-agent systems composed of first-order and second-order agents with unknown nonlinear dynamics and asymmetric input dead-zone under connected undirected topology. Under the proposed protocol and adaptive laws, a sliding mode variable for every agent converges to a compact set in finite time, and also the position errors and the velocity errors (for second-order agents) between any two agents converge to a small desired neighborhood of the origin in finite time. Each agent requires its states and the relative positions of its neighbors. By applying sliding mode control, the external disturbances, and the imperfect approximation of neural networks are rejected. The unknown terms of the agents’ dynamics are approximated using radial basis function neural networks. The adaptive compensator plus dead-zone is applied to overcome the asymmetric input dead-zone. Based on Lyapunov stability theory, analysis is led on stability. Different from the previous works, the global information graph is not used in the proposed protocol. Finally, our approach is examined for two examples to evaluate its performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".