Receding Horizon Stabilization and Disturbance Attenuation for Neural Networks with Time-Varying Delay

Choon Ki Ahn, Peng Shi, Ligang Wu

Research output: Contribution to journalArticlepeer-review

94 Citations (Scopus)

Abstract

This paper is concerned with the problems of receding horizon stabilization and disturbance attenuation for neural networks with time-varying delay. New delay-dependent conditions on the terminal weighting matrices of a new finite horizon cost functional for receding horizon stabilization are established for neural networks with time-varying or time-invariant delays using single-and double-integral Wirtinger-type inequalities. Based on the results, delay-dependent sufficient conditions for the receding horizon disturbance attenuation are given to guarantee the infinite horizon H performance of neural networks with time-varying or time-invariant delays. Three numerical examples are provided to illustrate the effectiveness of the proposed approach.

Original languageEnglish
Article number6999917
Pages (from-to)2680-2692
Number of pages13
JournalIEEE Transactions on Cybernetics
Volume45
Issue number12
DOIs
Publication statusPublished - 2015 Dec

Keywords

  • Cost functional
  • Time delay
  • disturbance attenuation
  • neural network
  • receding horizon stabilization

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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