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Intermittent Event-Triggered Optimal Leader-Following Consensus for Nonlinear Multi-Agent Systems Via Actor-Critic

Chen Liu, Lei Liu, Jinde Cao

IEEE Transactions on Neural Networks and Learning Systems

|November 1, 2021

Summary

This study introduces an intermittent event-triggered control for nonlinear multi-agent systems (MASs) using actor-critic methods. It ensures optimal leader-following consensus while preventing Zeno behavior.

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Area of Science:

Control Theory
Artificial Intelligence
Networked Systems

Background:

Multi-agent systems (MASs) require coordinated behavior for complex tasks.
Achieving consensus in nonlinear MASs with optimal control is challenging.
Event-triggered control reduces communication and computation load.

Purpose of the Study:

To develop an intermittent event-triggered optimal control strategy for leader-following consensus in nonlinear MASs.
To prove the optimality and convergence of the proposed control scheme.
To design an actor-critic network for approximate optimal control and exclude Zeno behavior.

Main Methods:

A novel distributed intermittent event-triggered control strategy is proposed.
A piecewise differential inequality is used to establish consensus criteria.
Policy iteration and Lyapunov stability theory are employed for optimality and convergence proofs.
An actor-critic network updates weights only at trigger instants.

Main Results:

A sufficient criterion for leader-following consensus in nonlinear MASs is derived.
The optimality of the multi-agent system (MAS) control is proven.
The convergence of the closed-loop system is demonstrated.
The proposed actor-critic network effectively implements approximate optimal control.
Zeno behavior is successfully excluded.

Conclusions:

The developed intermittent event-triggered control strategy ensures optimal leader-following consensus in nonlinear MASs.
The actor-critic approach provides an efficient method for approximate optimal control in MASs.
The scheme effectively balances performance and resource utilization by avoiding continuous communication.