Adaptive Leaderless Consensus Control of Strict-Feedback Nonlinear Multiagent Systems With Unknown Control Directions
Why this work is in the frame
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Bibliographic record
Abstract
The leaderless consensus problem over strict-feedback nonlinear multiagent systems (MASs) with unknown model parameters and control directions is investigated. The main idea of the existing consensus strategies for strict-feedback nonlinear MASs with unknown control directions is leading agents toward predefined global leaders/exosystems. However, in several missions, agents need to reach autonomous agreement on an a priori unknown quantity for a desired state, and hence the existing results are not applicable in these missions. The main contribution of this article is designing an adaptive leaderless consensus control scheme for strict-feedback nonlinear MASs when agents' control directions are unknown and unidentical. First, we introduce decentralized local error surfaces designed based on each agent position and neighboring agents' positions. We show that as the error surfaces remain bounded and converge to zero, the boundedness of the agents' positions and achieving leaderless consensus in the MAS can be guaranteed. Then, based on the properties of the Nussbaum-type functions, a decentralized backstepping adaptive control law is proposed under which the local error surfaces remain bounded and converge to zero. Finally, the design is more clarified and evaluated via an example.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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