Receding horizon disturbance attenuation for Takagi-Sugeno fuzzy switched dynamic neural networks

Choon Ki Ahn

doi:10.1016/j.ins.2014.04.024

Receding horizon disturbance attenuation for Takagi-Sugeno fuzzy switched dynamic neural networks

Choon Ki Ahn^*

^*Corresponding author for this work

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

54 Citations (Scopus)

Abstract

In this paper, we propose a new receding horizon disturbance attenuator (RHDA) for Takagi-Sugeno (T-S) fuzzy switched Hopfield neural networks with external disturbance. First, a new set of linear matrix inequality (LMI) conditions is proposed for the finite terminal weighting matrix of the receding horizon cost function with a cross term. Second, under this condition, we show that the proposed RHDA attenuates the effect of external disturbance on T-S fuzzy switched Hopfield neural networks with a guaranteed infinite horizon ℋ_∞ performance. In addition, we prove that the proposed RHDA guarantees internal stability in closed-loop systems. A numerical example is presented to describe the effectiveness of the proposed RHDA scheme.

Original language	English
Pages (from-to)	53-63
Number of pages	11
Journal	Information Sciences
Volume	280
DOIs	https://doi.org/10.1016/j.ins.2014.04.024
Publication status	Published - 2014 Oct 1

Keywords

Fuzzy system model
Linear matrix inequality (LMI)
Neuro-fuzzy system
Receding horizon disturbance attenuator (RHDA)
Switched neural network

ASJC Scopus subject areas

Software
Control and Systems Engineering
Theoretical Computer Science
Computer Science Applications
Information Systems and Management
Artificial Intelligence

Access to Document

10.1016/j.ins.2014.04.024

Cite this

@article{063c50d031fa41b59c946ead690152cf,

title = "Receding horizon disturbance attenuation for Takagi-Sugeno fuzzy switched dynamic neural networks",

abstract = "In this paper, we propose a new receding horizon disturbance attenuator (RHDA) for Takagi-Sugeno (T-S) fuzzy switched Hopfield neural networks with external disturbance. First, a new set of linear matrix inequality (LMI) conditions is proposed for the finite terminal weighting matrix of the receding horizon cost function with a cross term. Second, under this condition, we show that the proposed RHDA attenuates the effect of external disturbance on T-S fuzzy switched Hopfield neural networks with a guaranteed infinite horizon ℋ∞ performance. In addition, we prove that the proposed RHDA guarantees internal stability in closed-loop systems. A numerical example is presented to describe the effectiveness of the proposed RHDA scheme.",

keywords = "Fuzzy system model, Linear matrix inequality (LMI), Neuro-fuzzy system, Receding horizon disturbance attenuator (RHDA), Switched neural network",

author = "Ahn, \{Choon Ki\}",

year = "2014",

month = oct,

day = "1",

doi = "10.1016/j.ins.2014.04.024",

language = "English",

volume = "280",

pages = "53--63",

journal = "Information Sciences",

issn = "0020-0255",

publisher = "Elsevier Inc.",

}

TY - JOUR

T1 - Receding horizon disturbance attenuation for Takagi-Sugeno fuzzy switched dynamic neural networks

AU - Ahn, Choon Ki

PY - 2014/10/1

Y1 - 2014/10/1

N2 - In this paper, we propose a new receding horizon disturbance attenuator (RHDA) for Takagi-Sugeno (T-S) fuzzy switched Hopfield neural networks with external disturbance. First, a new set of linear matrix inequality (LMI) conditions is proposed for the finite terminal weighting matrix of the receding horizon cost function with a cross term. Second, under this condition, we show that the proposed RHDA attenuates the effect of external disturbance on T-S fuzzy switched Hopfield neural networks with a guaranteed infinite horizon ℋ∞ performance. In addition, we prove that the proposed RHDA guarantees internal stability in closed-loop systems. A numerical example is presented to describe the effectiveness of the proposed RHDA scheme.

AB - In this paper, we propose a new receding horizon disturbance attenuator (RHDA) for Takagi-Sugeno (T-S) fuzzy switched Hopfield neural networks with external disturbance. First, a new set of linear matrix inequality (LMI) conditions is proposed for the finite terminal weighting matrix of the receding horizon cost function with a cross term. Second, under this condition, we show that the proposed RHDA attenuates the effect of external disturbance on T-S fuzzy switched Hopfield neural networks with a guaranteed infinite horizon ℋ∞ performance. In addition, we prove that the proposed RHDA guarantees internal stability in closed-loop systems. A numerical example is presented to describe the effectiveness of the proposed RHDA scheme.

KW - Fuzzy system model

KW - Linear matrix inequality (LMI)

KW - Neuro-fuzzy system

KW - Receding horizon disturbance attenuator (RHDA)

KW - Switched neural network

UR - https://www.scopus.com/pages/publications/84902528860

U2 - 10.1016/j.ins.2014.04.024

DO - 10.1016/j.ins.2014.04.024

M3 - Article

AN - SCOPUS:84902528860

SN - 0020-0255

VL - 280

SP - 53

EP - 63

JO - Information Sciences

JF - Information Sciences

ER -

Receding horizon disturbance attenuation for Takagi-Sugeno fuzzy switched dynamic neural networks

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this