Consensus Seeking in Large-Scale Multiagent Systems With Hierarchical Switching-Backbone Topology.

Area of Science:

• Control Theory
• Network Science
• Distributed Systems

Background:

• Multiagent consensus problems are critical in distributed systems, with performance heavily influenced by network topology as agent numbers grow.
• Existing peer-to-peer architectures often lead to slow convergence due to uniform agent treatment and direct neighbor communication.

Purpose of the Study:

• To develop a novel hierarchical framework for multiagent systems (MAS) to enhance consensus convergence speed and stability.
• To address the limitations of traditional peer-to-peer architectures in large-scale agent networks.

Main Methods:

• Extraction of backbone network topology to establish a hierarchical organization within the MAS.
• Introduction of a geometric convergence method utilizing constraint sets (CS) with periodically updated switching-backbone topologies.
• Development of a fully decentralized framework named hierarchical switching-backbone MAS (HSBMAS).

Main Results:

• The HSBMAS framework demonstrates provable connectivity and convergence guarantees for connected initial topologies.
• Simulations across diverse topologies and densities confirm the framework's superior performance.
• The hierarchical structure and geometric convergence method significantly improve convergence speed.

Conclusions:

• The proposed HSBMAS framework offers a more efficient and stable approach to achieving consensus in large-scale multiagent systems.
• Hierarchical organization and dynamic backbone switching are effective strategies for optimizing consensus protocols.
• This work provides a robust foundation for future research in decentralized control and network optimization.