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 a sampled-data neural-network-based control system. A continuous-time nonlinear plant and a sampled-data three-layer fully connected feedforward neural-network-based controller are connected in a closed loop to perform the 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 largest sampling period and the 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
Pages (from-to)995-1005
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume36
Issue number5
DOIs
Publication statusPublished - 1 Oct 2006

Keywords

  • Neural network
  • Nonlinear system
  • Sampled-data control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Human-Computer Interaction

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