Adaptive connectivity control in networked multi-agent systems: A distributed approach.

Area of Science:

• Multi-agent systems
• Network science
• Control theory

Background:

• Effective communication is vital for cooperative networked multi-agent systems.
• Current methods lack dynamic monitoring and adjustment of communication topologies for balancing connectivity and energy efficiency.

Purpose of the Study:

• To propose a distributed approach for estimating and controlling system connectivity in multi-agent systems.
• To address the gap in dynamically managing communication topologies for optimal performance.

Main Methods:

• A modified consensus protocol for distributed estimation of a global weighted adjacency matrix.
• Utilizing algebraic connectivity (the second smallest eigenvalue of the graph Laplacian) to measure system connectivity.
• An adaptive mechanism to expedite consensus convergence and an analytical method for connectivity control using Fiedler vector approximation.

Main Results:

• The proposed method effectively estimates and tracks algebraic connectivity in dynamic multi-agent systems.
• Demonstrated robust connectivity maintenance against external disturbances and agent failures.
• Identified and eliminated non-contributory links, improving long-term energy efficiency.

Conclusions:

• The developed distributed approach enhances communication efficiency and robustness in cooperative networked multi-agent systems.
• The strategy offers practical utility for real-world applications requiring dynamic network management.
• This work provides a foundation for more intelligent and adaptive multi-agent communication strategies.