An Overview of Cooperative and Consensus Control of Multiagent Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There has been a growing interest toward the development of networked unmanned autonomous systems that can operate without an extensive involvement of humans. The motivation for this focus can be traced to the emergence of applications where direct human intervention is not possible due to the environmental hazards, complexity of the tasks, or other restrictions. These networks can consist of a large number of dynamical systems (agents), such as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and unmanned underwater vehicles (UUVs). These systems commonly include a number of sensors, actuators, and decisionmakers. Therefore, the network of these systems is a network of a large number of sensors and actuators or, as is known in the literature, a system of systems (SoS). In this work, we provide a brief overview of controlling these networks, their applications, and the solutions that are proposed for these problems in the literature. Specifically, this article overviews recent results and progress made on multiagent consensus by focusing on cooperative control and consensus (formation) control in the presence of communication, control implementation, and saturation constraints. In addition, we review the active areas of event‐triggered consensus control, network security and attacks on the agents, and networks and resilient consensus control strategies that are proposed to handle these challenges.
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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 | 0.000 | 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