Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties. Academic Article uri icon


  • 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.

published proceedings

  • IEEE Trans Neural Netw Learn Syst

author list (cited authors)

  • Liang, H., Liu, G., Zhang, H., & Huang, T.

citation count

  • 284

complete list of authors

  • Liang, Hongjing||Liu, Guangliang||Zhang, Huaguang||Huang, Tingwen

publication date

  • May 2021