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Fully distributed data-driven model-free adaptive control for consensus tracking in multi-agent systems.

Sayed Shahab Aldin Sahafi1, Malihe Maghfoori Farsangi1

  • 1Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

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Summary
This summary is machine-generated.

This study presents a fully distributed model-free adaptive control (MFAC) for multi-agent systems (MASs). The novel approach uses only local information for faster consensus tracking, even with dynamic agent participation.

Keywords:
Consensus controlDistributed controlModel-free adaptive control (MFAC)Multi-agent systems (MASs)

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Distributed Computing

Background:

  • Multi-agent systems (MASs) often rely on centralized or complex distributed control strategies.
  • Existing model-free adaptive control (MFAC) methods for MAS consensus tracking typically require global communication graph knowledge.
  • Dynamic changes in agent membership and communication topology pose significant challenges for MAS control.

Purpose of the Study:

  • To develop a fully distributed model-free adaptive control (MFAC) strategy for consensus tracking in multi-agent systems (MASs).
  • To enable agents to achieve consensus using only local neighbor information, eliminating the need for global communication graph knowledge.
  • To design a control method that is robust to dynamic changes in MAS topology, such as agents joining or leaving.

Main Methods:

  • Implementation of a fully distributed MFAC approach.
  • Utilization of compact form data linearization (CFDL) for controller configuration.
  • Control strategy relies solely on local information exchanged between neighboring agents.
  • Relaxation of strongly connected graph requirements, allowing for spanning tree-based consensus.

Main Results:

  • Achieved fully distributed consensus tracking in MASs.
  • Demonstrated successful control with only local information, enhancing scalability and adaptability.
  • Showcased robustness to dynamic agent addition/removal.
  • Validated faster convergence to desired trajectories compared to existing MFAC methods through simulations.

Conclusions:

  • The proposed fully distributed MFAC approach offers a scalable and robust solution for consensus tracking in MASs.
  • Local information-based control significantly simplifies implementation and enhances adaptability to network changes.
  • The method provides a faster and more efficient alternative to previous MFAC techniques for MAS consensus.
  • This work advances the field of distributed control for complex multi-agent coordination problems.