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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

    Author(s)
    Han, Qing-Long
    Liu, Yurong
    Yang, Fuwen
    Griffith University Author(s)
    Yang, Fuwen
    Year published
    2016
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    Abstract
    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 ...
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    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.
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    Journal Title
    IEEE Transactions on Neural Networks and Learning Systems
    Volume
    27
    Issue
    2
    DOI
    https://doi.org/10.1109/TNNLS.2015.2411290
    Subject
    Automation engineering
    Publication URI
    http://hdl.handle.net/10072/173012
    Collection
    • Journal articles

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