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Data-driven optimal cooperative tracking control for heterogeneous multi-agent systems.

Yong-Sheng Ma1, Yong Xu1, Jian Sun1

  • 1National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China.

ISA Transactions
|September 12, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new control method for multi-agent systems to achieve optimal cooperative tracking. It enables followers to track leaders without direct communication, using a novel observer and decentralized controller.

Keywords:
Fully distributed observerHeterogeneous multi-agent systemsPrescribed timeReinforcement learning

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Area of Science:

  • Control Systems Engineering
  • Robotics
  • Artificial Intelligence

Background:

  • Cooperative tracking is crucial for multi-agent systems.
  • Existing methods often require leader-follower communication or system models.
  • Heterogeneous systems present unique challenges in control design.

Purpose of the Study:

  • To develop a data-driven hierarchical control scheme for optimal cooperative tracking in heterogeneous multi-agent systems.
  • To address the challenge of followers tracking leaders without direct communication.
  • To design a control system that does not require prior knowledge of system models or stabilizing policies.

Main Methods:

  • A prescribed-time fully distributed observer estimates the leader's state for each follower.
  • A data-driven decentralized controller is designed for output tracking.
  • The control scheme is hierarchical, integrating observer and controller design.

Main Results:

  • The observer guarantees convergence within a designer-predetermined time, independent of global topology.
  • The decentralized controller achieves leader-follower tracking without needing follower system models.
  • Simulation results validate the effectiveness and advantages of the proposed method.

Conclusions:

  • The proposed hierarchical control scheme offers a robust solution for data-driven optimal cooperative tracking.
  • The method overcomes limitations of existing approaches by eliminating the need for leader-follower communication and system model knowledge.
  • This work advances the field of multi-agent systems control, particularly for heterogeneous configurations.