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


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.

Copyright © 2007 by American Society of Mechanical Engineers
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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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