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Exponential stabilization for sampled-data neural-network-based control systems.

Authors
  • Wu, Zheng-Guang
  • Shi, Peng
  • Su, Hongye
  • Chu, Jian
Type
Published Article
Journal
IEEE Transactions on Neural Networks and Learning Systems
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Dec 01, 2014
Volume
25
Issue
12
Pages
2180–2190
Identifiers
DOI: 10.1109/TNNLS.2014.2306202
PMID: 25420241
Source
Medline
License
Unknown

Abstract

This paper investigates the problem of sampled-data stabilization for neural-network-based control systems with an optimal guaranteed cost. Using time-dependent Lyapunov functional approach, some novel conditions are proposed to guarantee the closed-loop systems exponentially stable, which fully use the available information about the actual sampling pattern. Based on the derived conditions, the design methods of the desired sampled-data three-layer fully connected feedforward neural-network-based controller are established to obtain the largest sampling interval and the smallest upper bound of the cost function. A practical example is provided to demonstrate the effectiveness and feasibility of the proposed techniques.

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