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

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

Research output: Contribution to journalArticlepeer-review


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
JournalIEEE Transactions on Network Science and Engineering
Publication statusAccepted/In press - 2022


  • adaptive asymptotic tracking
  • Adaptive control
  • Artificial neural networks
  • Event-triggered control (ETC)
  • Lyapunov methods
  • networked nonlinear stochastic systems
  • neural networks (NNs)
  • Process control
  • Stochastic processes
  • Stochastic systems
  • Uncertainty

ASJC Scopus subject areas

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


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