Decentralized Stabilization of Neural Network Linearly Interconnected Systems via T-S Fuzzy Control-Reference-Cited by-同舟云学术

Decentralized Stabilization of Neural Network Linearly Interconnected Systems via T-S Fuzzy Control

Published:2005-08-09 Issue:3 Volume:129 Page:343-351
ISSN:0022-0434
Container-title:Journal of Dynamic Systems, Measurement, and Control
language:en
Short-container-title:

Author:

Hsiao Feng-Hsiag¹,Liang Yew-Wen²,Xu Sheng-Dong²,Lee Gwo-Chuan³

Affiliation:

1. Department of Electronic Engineering, National University of Tainan, No. 33, Section 2, Shu Lin Street, Tainan 700, Taiwan, Republic of China

2. Department of Electrical and Control Engineering, National Chiao Tung University, No. 1001, Ta Hsueh Road, Hsinchu 30010, Taiwan, Republic of China

3. Department of Computer Science and Information Engineering, National United University, No. 1, Lien Da, Kung Ching Li, Miaoli 360, Taiwan, Republic of China

Abstract

Abstract The stabilization problem is considered in this study for a neural-network (NN) linearly interconnected system that consists of a number of NN models. First, a linear difference inclusion (LDI) state-space representation is established for the dynamics of each NN model. Then, based on the LDI state-space representation, a stability criterion in terms of Lyapunov’s direct method is derived to guarantee the asymptotic stability of closed-loop NN linearly interconnected systems. Subsequently, according to this criterion and the decentralized control scheme, a set of Takagi-Sugeno (T-S) fuzzy controllers is synthesized to stabilize the NN linearly interconnected system. Finally, a numerical example with simulations is given to demonstrate the concepts discussed throughout this paper.

Publisher

ASME International

Subject

Computer Science Applications,Mechanical Engineering,Instrumentation,Information Systems,Control and Systems Engineering

Link

http://asmedigitalcollection.asme.org/dynamicsystems/article-pdf/129/3/343/6733940/343_1.pdf

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