Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties

TLDR

The study develops an adaptive event‑triggered neural control strategy for nonaffine pure‑feedback nonlinear multiagent systems with dynamic disturbances, unmodeled dynamics, and dead‑zone inputs, aiming to reduce communication load via a varying‑threshold trigger. Using radial basis function neural networks to approximate unknown nonlinearities, a dynamic signal to handle unmodeled dynamics, and a Lyapunov‑based adaptive control design, the authors construct an event‑triggered protocol that guarantees bounded signals and follower convergence to a neighborhood of the leader. The proposed protocol ensures that all follower outputs converge to a neighborhood of the leader’s output while keeping all closed‑loop signals bounded.

Abstract

This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. Radial basis function neural networks are applied to approximate the unknown nonlinear function. A dynamic signal is constructed to deal with the design difficulties in the unmodeled dynamics. Moreover, to reduce the communication burden, we propose an event-triggered strategy with a varying threshold. Based on the Lyapunov function method and adaptive neural control approach, a novel event-triggered control protocol is constructed, which realizes that the outputs of all followers converge to a neighborhood of the leader's output and ensures that all signals are bounded in the closed-loop system. An illustrative simulation example is applied to verify the usefulness of the proposed algorithms.