Leader-Following Consensus of Nonlinear Multiagent Systems With Stochastic Sampling.

Area of Science:

• Control Theory
• Networked Systems
• Robotics

Background:

• Achieving consensus in multiagent systems is crucial for coordinated behavior.
• Sampled-data systems introduce challenges due to discrete-time information.
• Leader-following consensus requires a subset of agents to guide the others.

Purpose of the Study:

• To develop a distributed consensus protocol for leader-following consensus in nonlinear multiagent systems.
• To address the complexities introduced by probabilistic, sampled-data control.
• To provide a scalable solution applicable to large-scale networks.

Main Methods:

• A distributed consensus protocol with probabilistic sampling across two periods is proposed.
• Consensus criteria are derived for both directed and undirected graph topologies.
• The problem is transformed into a master-slave synchronization problem for dimension reduction.

Main Results:

• A general consensus criterion is established for directed graphs.
• A dimension-reduced condition is derived for undirected graphs.
• The protocol's effectiveness is validated through theoretical analysis and two illustrative examples.

Conclusions:

• The proposed protocol enables efficient leader-following consensus in sampled-data systems with nonlinear agents.
• The dimension-reduction technique simplifies analysis and enhances scalability for large networks.
• Network topology plays a significant role in achieving consensus, offering avenues for network design.