Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1441
Title: Design and stabilization of sampled-data neural-network-based control systems
Authors: Lam, HK
Leung, FHF 
Keywords: Neural network
Nonlinear system
Sampled-data control
Issue Date: Oct-2006
Publisher: IEEE
Source: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, Oct. 2006, v. 36, no. 5, p. 995-1005 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics 
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.
URI: http://hdl.handle.net/10397/1441
ISSN: 1083-4419
DOI: 10.1109/IJCNN.2005.1556251
Rights: © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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