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Nonlinear Control and Filtering for Stochastic Networked Systems

List Price: $73.99
SKU:
9780367656867
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  • Product Details

    Author:
    Lifeng Ma, Zidong Wang, Yuming Bo
    Format:
    Paperback
    Pages:
    242
    Publisher:
    CRC Press (September 30, 2020)
    Language:
    English
    Audience:
    Professional and scholarly
    ISBN-13:
    9780367656867
    Weight:
    16oz
    Dimensions:
    6.125" x 9.1875"
    File:
    TAYLORFRANCIS-TayFran_260716044426388-20260716.xml
    Folder:
    TAYLORFRANCIS
    List Price:
    $73.99
    Country of Origin:
    United States
    Case Pack:
    1
    As low as:
    $70.29
    Publisher Identifier:
    P-CRC
    Discount Code:
    H
    Pub Discount:
    30
    Imprint:
    CRC Press
  • Overview

    In this book, control and filtering problems for several classes of stochastic networked systems are discussed. In each chapter, the stability, robustness, reliability, consensus performance, and/or disturbance attenuation levels are investigated within a unified theoretical framework. The aim is to derive the sufficient conditions such that the resulting systems achieve the prescribed design requirements despite all the network-induced phenomena. Further, novel notions such as randomly occurring sensor failures and consensus in probability are discussed. Finally, the theories/techniques developed are applied to emerging research areas.



    Key Features







    • Unifies existing and emerging concepts concerning stochastic control/filtering and distributed control/filtering with an emphasis on a variety of network-induced complexities






    • Includes concepts like randomly occurring sensor failures and consensus in probability (with respect to time-varying stochastic multi-agent systems)






    • Exploits the recursive linear matrix inequality approach, completing the square method, Hamilton-Jacobi inequality approach, and parameter-dependent matrix inequality approach to handle the emerging mathematical/computational challenges






    • Captures recent advances of theories, techniques, and applications of stochastic control as well as filtering from an engineering-oriented perspective






    • Gives simulation examples in each chapter to reflect the engineering practice