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Data-based bipartite formation control for multi-agent systems with communication constraints.

Juqin Wang1, Huarong Zhao2, Hongnian Yu3

  • 1School of Internet of Things, Wuxi Institute of Technology, Wuxi, China.

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|February 16, 2024
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Summary
This summary is machine-generated.

This study introduces a novel quantized data-driven control for multi-agent systems, ensuring accurate bipartite formation despite communication limits and dynamic network structures.

Keywords:
Data-driven controlbipartite formationdata quantizationmulti-agent systemssensor saturation

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Area of Science:

  • Control Systems Engineering
  • Distributed Systems
  • Networked Multi-Agent Systems

Background:

  • Multi-agent systems (MAS) face challenges in achieving coordinated behaviors under communication constraints.
  • Bipartite formation control is crucial for cooperative-competitive interactions in MAS.
  • Existing methods often require full system dynamics or lack robustness to topology changes.

Purpose of the Study:

  • To develop a data-driven distributed bipartite formation control strategy for discrete-time MAS.
  • To address communication constraints, including quantization and saturation.
  • To accommodate both fixed and switching communication topologies in MAS.

Main Methods:

  • A quantized data-driven distributed bipartite formation control approach is proposed.
  • Dynamic linearization is used to establish time-varying linear data models for agents.
  • Control scheme constructed using incomplete input-output data, without requiring explicit system dynamics.

Main Results:

  • The proposed algorithm guarantees convergence of bipartite formation tracking errors to the origin.
  • The approach is effective even with time-varying switching communication topologies.
  • Simulation and hardware tests validate the scheme's performance.

Conclusions:

  • The developed quantized data-driven control is effective for distributed bipartite formation in MAS.
  • The method offers robustness against communication constraints and dynamic network topologies.
  • This work advances control strategies for complex networked systems.