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  • Distributed H∞-consensus fault detection for fuzzy systems with faults, switching network topology, packet dropouts and channel fading

    Author(s)
    Qu, H
    Yang, F
    Han, QL
    Griffith University Author(s)
    Yang, Fuwen
    Year published
    2018
    Metadata
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    Abstract
    This paper is concerned with the distributed H∞-consensus fault detection (FD) filtering problem for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with faults, switching network topology, channel fading and different communication channels-induced packet dropouts with different missing rates. The purpose of the addressed problem is to design a distributed H∞-consensus FD filter to guarantee the sensitivity of the residual signal to the faults and the robustness of the residual system to effects of both switching network topology, channel fading and different communication channels-induced packet dropouts with ...
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    This paper is concerned with the distributed H∞-consensus fault detection (FD) filtering problem for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with faults, switching network topology, channel fading and different communication channels-induced packet dropouts with different missing rates. The purpose of the addressed problem is to design a distributed H∞-consensus FD filter to guarantee the sensitivity of the residual signal to the faults and the robustness of the residual system to effects of both switching network topology, channel fading and different communication channels-induced packet dropouts with different lossing rates. On the basis of the T-S fuzzy approach and the Lyapunov functional, distributed H∞-consensus FD filter design criterion is derived such that the residual system is exponentially stable in the mean square, and the optimal H∞ filtering performance index is derived. A simulation example is conducted to verify the usefulness of the proposed FD filter design approach.
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    Conference Title
    2018 Australian & New Zealand Control Conference (ANZCC)
    DOI
    https://doi.org/10.1109/ANZCC.2018.8606605
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/393365
    Collection
    • Conference outputs

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