Event-Based Adaptive Neural Asymptotic Tracking Control for Networked Nonlinear Stochastic Systems

Yuan Xin Li, Xiao Yan Hu, Choon Ki Ahn, Zhong Sheng Hou, Hyun Ho Kang

Research output: Contribution to journalArticlepeer-review

Abstract

This paper investigates the adaptive asymptotic tracking control for networked nonlinear stochastic systems. Different from having the necessity of prior knowledge of the unknown control coefficients in the conventional adaptive control of nonlinear stochastic systems, in this study, the limitation of control coefficients in the stability analysis is relaxed by constructing a new Lyapunov function that contains the lower bounds of the control gain function. By constructing a smooth function with a positive time-varying integral function and utilizing the boundary estimation method, asymptotic tracking control can be guaranteed. At the same time, for nonlinear stochastic systems with unknown control coefficients, a neural adaptive event-triggered strategy that greatly saves communication resources while ensuring system performance is proposed. Finally, simulation results show that the proposed control scheme can guarantee the realization of the control objectives.

Original languageEnglish
Pages (from-to)2290-2300
Number of pages11
JournalIEEE Transactions on Network Science and Engineering
Volume9
Issue number4
DOIs
Publication statusPublished - 2022

Keywords

  • Event-triggered control (ETC)
  • adaptive asymptotic tracking
  • networked nonlinear stochastic systems
  • neural networks (NNs)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Event-Based Adaptive Neural Asymptotic Tracking Control for Networked Nonlinear Stochastic Systems'. Together they form a unique fingerprint.

Cite this