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TECHNICAL BRIEFS

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

[+] Author and Article Information
Feng-Hsiag Hsiao1

Department of Electronic Engineering, National University of Tainan, No. 33, Section 2, Shu Lin Street, Tainan 700, Taiwan, Republic of Chinahsiao.a00001@msa.hinet.net

Yew-Wen Liang, Sheng-Dong Xu

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

Gwo-Chuan Lee

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

1

Author to whom correspondence should be addressed.

J. Dyn. Sys., Meas., Control 129(3), 343-351 (Aug 09, 2005) (9 pages) doi:10.1115/1.2234492 History: Received October 19, 2004; Revised August 09, 2005

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.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

The lth NN model

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Figure 2

The first NN model

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Figure 3

The state x1(k) of subsystem 1

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Figure 4

The state x2(k) of subsystem 2

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Figure 5

The state x3(k) of subsystem 3

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