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Optimal communication network-based H∞ quantized control with packet dropouts for a class of discrete-time neural networks with distributed time delay

journal contribution
posted on 2018-07-20, 00:00 authored by Qing-Long Han, Yurong Liu, F Yang
This paper is concerned with optimal communication network-based H∞ quantized control for a discrete-Time neural network with distributed time delay. Control of the neural network (plant) is implemented via a communication network. Both quantization and communication network-induced data packet dropouts are considered simultaneously. It is assumed that the plant state signal is quantized by a logarithmic quantizer before transmission, and communication network-induced packet dropouts can be described by a Bernoulli distributed white sequence. A new approach is developed such that controller design can be reduced to the feasibility of linear matrix inequalities, and a desired optimal control gain can be derived in an explicit expression. It is worth pointing out that some new techniques based on a new sector-like expression of quantization errors, and the singular value decomposition of a matrix are developed and employed in the derivation of main results. An illustrative example is presented to show the effectiveness of the obtained results. © 2012 IEEE.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

27

Issue

2

Start Page

426

End Page

434

Number of Pages

9

eISSN

2162-2388

ISSN

2162-237X

Publisher

Institute of Electrical and Electronics Engineers

Peer Reviewed

  • Yes

Open Access

  • No

External Author Affiliations

Griffith University

Author Research Institute

  • Centre for Intelligent Systems

Era Eligible

  • Yes

Journal

IEEE Transactions on Neural Networks and Learning Systems