Decentralized stabilization of neural network linearly interconnected systems via T-S fuzzy control

Feng Hsiag Hsiao*, Yew-Wen Liang, Sheng Dong Xu, Gwo Chuan Lee

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

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.

Original languageEnglish
Pages (from-to)343-351
Number of pages9
JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Volume129
Issue number3
DOIs
StatePublished - 1 May 2007

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