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

Choon Ki Ahn; Peng Shi; Ligang Wu

doi:10.1109/TCYB.2014.2381604

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

Choon Ki Ahn, Peng Shi, Ligang Wu

Research output: Contribution to journal › Article › peer-review

135 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 language	English
Article number	6999917
Pages (from-to)	2680-2692
Number of pages	13
Journal	IEEE Transactions on Cybernetics
Volume	45
Issue number	12
DOIs	https://doi.org/10.1109/TCYB.2014.2381604
Publication status	Published - 2015 Dec

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

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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10.1109/TCYB.2014.2381604

Cite this

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title = "Receding Horizon Stabilization and Disturbance Attenuation for Neural Networks with Time-Varying Delay",

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.",

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AB - 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.

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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