Distributed Finite-Time Bipartite Consensus Control for Constrained Nonlinear MASs: A Switched Function Approach Based on Prioritized Strategy
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Bibliographic record
Abstract
The distributed finite-time bipartite consensus control problem for nonlinear multi-agent systems (MASs) is discussed in this work. This paper proposes the prioritized strategy that utilizes a more relaxed criteria, i.e., a signed digraph containing one spanning tree, to achieve bipartition among all agents in the system. To improve the scheme's applicability, a non-strict feedback structure is adopted for the agent model. Different from some existing finite-time control schemes, a novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C^{1}$</tex-math></inline-formula> finite-time fuzzy adaptive controller is proposed utilizing the smooth switched function. The singularity problem that arises from the differential virtual control law is effectively solved by constructing the switched function. With aid of the distributed backstepping technique, as well as barrier Lyapunov functions and finite-time control approach, all agents successfully attain bipartite consensus while maintaining bounded errors. Simulation results certify the validity of the scheme.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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