Design and stabilization of sampled-data neural-network-based control systems

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Abstract

This paper presents the design and stability analysis of sampled-data neural-network-based control systems. A continuous-time nonlinear plant and a sampled-data three-layer fully-connected feed-forward neural-network-based controller are connected in a closed-loop to perform a control task. Stability conditions will be derived to guarantee the closed-loop system stability. Linear-matrix-inequality- and genetic-algorithm-based approaches will be employed to obtain the maximum sampling period and connection weights of the neural network subject to the considerations of the system stability and performance. An application example will be given to illustrate the design procedure and effectiveness of the proposed approach.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages2249-2254
Number of pages6
Volume4
DOIs
Publication statusPublished - 1 Dec 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: 31 Jul 20054 Aug 2005

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2005
Country/TerritoryCanada
CityMontreal, QC
Period31/07/054/08/05

ASJC Scopus subject areas

  • Software

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