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

^*Corresponding author for this work

School of Astronautics

Korea University
Harbin Engineering University
Victoria University
University of Adelaide

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

139 Scopus citations

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
State	Published - Dec 2015

Keywords

Cost functional
Time delay
disturbance attenuation
neural network
receding horizon stabilization

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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.",

keywords = "Cost functional, Time delay, disturbance attenuation, neural network, receding horizon stabilization",

author = "Ahn, \{Choon Ki\} and Peng Shi and Ligang Wu",

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AU - Ahn, Choon Ki

AU - Shi, Peng

AU - Wu, Ligang

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N2 - 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.

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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KW - Time delay

KW - disturbance attenuation

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KW - receding horizon stabilization

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JO - IEEE Transactions on Cybernetics

JF - IEEE Transactions on Cybernetics

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M1 - 6999917

ER -

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

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Keywords

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