Event-Triggered Observer-Based Fixed-Time Consensus Control for Uncertain Nonlinear Multiagent Systems with Unknown States

TL;DR

This paper presents a novel event-triggered, observer-based fixed-time consensus control method for uncertain nonlinear multiagent systems, utilizing neural networks to handle unknown functions and ensure stability with reduced update frequency.

Contribution

It introduces a new fixed-time consensus control approach combining event-triggering, output feedback, and neural networks for uncertain nonlinear multiagent systems.

Findings

Achieves fixed-time consensus with stability guarantees.

Reduces update frequency via event-triggered strategy.

Validated effectiveness through simulation examples.

Abstract

This paper introduces a novel approach for achieving fixed-time tracking consensus control in multiagent systems (MASs). Departing from the reliance on traditional controllers, our innovative controller integrates modified tuning and Lyapunov functions to guarantee stability and convergence. Furthermore, we have implemented an event-triggered strategy aimed at reducing the frequency of updates, alongside an output-feedback observer to manage unmeasured states effectively. To address the challenges posed by unknown functions and algebraic-loop problems, we opted for radial basis function neural networks (RBF NNs), chosen for their superior performance. Our methodology successfully mitigates Zeno's behavior and ensures stability within a narrowly defined set. The efficacy of our proposed solution is validated through two illustrative simulation examples.