Distributed Adaptive Consensus of Physically Interconnected Multi-Agent Networks With Uncertainties

This paper considers the distributed cooperative control of physically interconnected multi-agent networks in the presence of nonlinear and unknown uncertainties that is one of the key problems in robot swarms and autonomous vehicle fleet. A distributed adaptive control framework is established, under which edge-based adaptive algorithms and node-based adaptive algorithms are designed, respectively. In the designed protocols, a linear term is used to force the states of all agents to be the same and a nonlinear term inspired by the sliding-mode control is introduced to suppress the effect of the undesirable uncertainties. Compared with the exiting related works, the algorithms presented here are applicable to physically interconnected systems, and they are scalable without requiring any global information and robust to various uncertainties. The results are also extended to the case of non-matching uncertainties. The effectiveness of the protocols is verified by an example of multiple coupled vehicles. demonstrating their potential to enhance the robustness and adaptability of robotic systems in dynamic and complex operational environments.Note to Practitioners—This research addresses the challenge of achieving consensus in physically interconnected multi-agent networks, such as autonomous vehicle fleets or robotic swarms, particularly in environments with uncertainties. Traditional methods often require global information, which can be impractical. Our study introduces a distributed adaptive control framework that allows these systems to operate effectively without global data. This framework uses edge-based and node-based adaptive algorithms to enhance system stability and adaptability. In this paper, we improve reliability and efficiency in coordinating multi-agent systems. For example, autonomous vehicles can maintain formation and synchronize movements despite environmental unpredictability. Similarly, robotic swarms can adapt to changing conditions during tasks like search and rescue. The fully distributed nature of our algorithms reduces computational and communication overhead, making them scalable for large-scale applications. Future work will explore extending these protocols to directed graphs and integrating event-triggered communication to further optimize performance.