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Related Experiment Video

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The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Distributed output formation tracking control of heterogeneous multi-agent systems using reinforcement learning.

Yu Shi¹, Xiwang Dong², Yongzhao Hua³

¹School of Automation Science and Electronic Engineering, Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing, 100191, PR China.

ISA Transactions

|March 16, 2023

Summary

This study addresses complex multi-agent systems by developing a model-free reinforcement learning controller for distributed time-varying output formation tracking. The novel approach ensures follower agents accurately track a virtual leader

Keywords:

Distributed trajectory generator Heterogeneous system Output formation tracking Reinforcement learning

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Area of Science:

Control Theory
Robotics
Artificial Intelligence

Background:

Heterogeneous multi-agent systems present challenges in coordinated control.
Distributed output formation tracking requires agents to follow a leader while maintaining relative configurations.

Purpose of the Study:

To solve the distributed time-varying output formation tracking problem for heterogeneous multi-agent systems.
To design a controller that does not require prior knowledge of follower dynamics.

Main Methods:

A distributed trajectory generator using neighboring interactions.
Model-free reinforcement learning for an optimal tracking controller.
Design of a compensational input based on learning results.

Main Results:

The learning process and controller stability are analyzed.
Output formation tracking error converges asymptotically to zero.
Numerical simulations validate the proposed scheme.

Conclusions:

The proposed model-free reinforcement learning approach effectively achieves distributed time-varying output formation tracking in heterogeneous multi-agent systems.
The method ensures accurate tracking and formation configuration without needing detailed system dynamics.