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

By Hongjing Liang, Guangliang Liu, Hua-guang Zhang, Tingwen Huang. IEEE Transactions on Neural Networks and Learning Systems, 2020

436 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/b6d3333cae5aa7c1bf785d14dba2bfc6581f880e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

  1. Yuanxin Li
  2. Shaocheng Tong
  3. Yongming Li
  4. Yong-ming Li
  5. Yanjun Liu
  6. Mou Chen

In Computer Networks and Communications

  1. Shaocheng Tong
  2. Yuanxin Li
  3. Yongming Li
  4. Yong-ming Li

In Control and Systems Engineering

  1. Yuanxin Li
  2. Shaocheng Tong
  3. Yong-ming Li
  4. Yanjun Liu
  5. Shuzhi Sam Ge
  6. Mou Chen
  7. Hongyi Li
  8. Peng Shi

Citations in the corpus