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Dynamic event-triggered optimized control for nonlinear multi-agent systems via reinforcement learning.

Xiaoli Ruan1, Shaowei Liang1, Tao Peng1

  • 1College of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China.

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|April 15, 2026
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Summary
This summary is machine-generated.

This study introduces an optimized consensus tracking control for multi-agent systems (MASs), reducing communication load and enhancing robustness against uncertainties using adaptive reinforcement learning and dynamic event-triggered control.

Keywords:
Dynamic event-triggered control (DETC)Nonlinear MASsOptimized controlReinforcement learning (RL)

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Networked Systems

Background:

  • Multi-agent systems (MASs) face challenges in practical deployment due to limited communication and environmental uncertainties.
  • Existing control strategies often struggle with resource constraints and unknown nonlinearities in MASs.

Purpose of the Study:

  • To develop an optimized consensus tracking control framework for high-order nonlinear MASs.
  • To address communication bottlenecks and environmental uncertainties in MASs deployment.
  • To enhance the robustness and efficiency of MASs in resource-constrained environments.

Main Methods:

  • Employed multilayer perceptrons (MLPs) as adaptive function approximators for unknown nonlinearities.
  • Utilized a multi-agent Actor-Critic reinforcement-learning (RL) mechanism for parameter tuning, incorporating consensus information.
  • Implemented a dynamic event-triggered control (DETC) mechanism to adaptively regulate sampling-error thresholds online.
  • Ensured closed-loop stability and prevented Zeno behavior using Lyapunov analysis.

Main Results:

  • The proposed framework significantly reduces communication overhead compared to independent learning approaches.
  • Demonstrated superior robustness against sensor noise in numerical studies on a multi-electromechanical system.
  • Effectively handled unknown nonlinearities and environmental uncertainties.

Conclusions:

  • The developed optimized consensus tracking control framework offers a reliable solution for resource-constrained networked control systems.
  • The integration of adaptive RL and DETC enhances the performance and practicality of MASs.
  • The approach provides a significant improvement over existing methods in terms of communication efficiency and robustness.