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Dynamic event-triggered approximate optimal consensus control for unknown nonlinear multi-agent systems via adaptive

Dehua Zhang1, Yao Hao1, Qingsong Yuan1

  • 1School of Artificial Intelligence, Henan University, No. 379, North Section of Mingli Road, Zhengzhou, 450046, Henan Province, China.

ISA Transactions
|March 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new control scheme for nonlinear multi-agent systems (MASs) using adaptive dynamic programming (ADP). The method enhances practical applications by reducing communication and computational needs while ensuring robust performance against disturbances.

Keywords:
Adaptive dynamic programmingCritic neural networkDynamic event-triggered mechanismNeural network state observerOptimal consensus control

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Robotics

Background:

  • Real-world systems often have unknown dynamics and limited sensor capabilities, hindering direct state measurement.
  • Communication and computational overhead are significant challenges in implementing control schemes for resource-constrained multi-agent systems (MASs).
  • Existing control methods may require restrictive conditions like persistent excitation (PE), limiting their practical applicability.

Purpose of the Study:

  • To develop a dynamic event-triggered approximate optimal consensus control scheme for nonlinear MASs with unknown dynamics.
  • To address the challenge of unmeasurable states in physical systems using a neural network (NN) state observer.
  • To reduce communication and computational burdens through a dynamic event-triggered mechanism (DETM) while eliminating Zeno behavior.

Main Methods:

  • A neural network (NN) state observer was designed to estimate unmeasurable states.
  • A disturbance-aware term was incorporated into the cost function for enhanced robustness.
  • A dynamic event-triggered mechanism (DETM) was integrated to optimize resource utilization.
  • A critic-only NN architecture was employed to approximate the Hamilton-Jacobi-Bellman (HJB) equation, relaxing the PE condition.

Main Results:

  • The proposed scheme achieved stable consensus tracking in nonlinear MASs.
  • Robust disturbance rejection capabilities were demonstrated.
  • Efficient resource utilization was confirmed through reduced communication and computation.
  • The scheme proved effective and practical in physics-relevant case studies, including industrial processes and manipulator systems.

Conclusions:

  • The developed dynamic event-triggered approximate optimal consensus control scheme offers a practical solution for nonlinear MASs with unknown dynamics.
  • The integration of NN state observers and DETM enhances robustness and efficiency for real-world applications.
  • The critic-only NN approach simplifies implementation and reduces computational load for resource-limited systems.