• DocumentCode
    1797460
  • Title

    On the cooperative observability of a continuous-time linear system on an undirected network

  • Author

    Henghui Zhu ; Kexin Liu ; Jinhu Lu ; Zongli Lin ; Yao Chen

  • Author_Institution
    Key Lab. of Syst. & Control, Acad. of Math. & Syst. Sci., Beijing, China
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    2940
  • Lastpage
    2944
  • Abstract
    In traditional control theory, a single observer has access all the measured outputs of the plant to estimates its asymptotically. In many real world engineering systems, it may be difficult to build a single observer that has access to all the measured outputs. One way around this difficulty is to build a network of cooperative observers, each of which obtains a portion of the measurement outputs, that collectively produce an asymptotic estimate of the plant state. In this paper, we construct a network of such observers for a continuous-time linear system. Assuming that these observers are connected through an undirected connected network, we establish a necessary and sufficient condition on the plant parameters under which the network of observers will achieve asymptotic omniscience. A network of cooperative observers is said to achieve asymptotic omniscience if their states all converge to the plant state asymptotically. Numerical simulation results are presented to validate theoretical results. The design of cooperative observers sheds some light on the solution of some other real-world problems, such as the design of networked location-based services and sensor networks.
  • Keywords
    continuous time systems; control system synthesis; linear systems; network theory (graphs); observability; asymptotic omniscience; asymptotic plant state estimate; continuous-time linear system; control theory; cooperative observability; cooperative observer design; networked location-based services; sensor networks; single observer; undirected connected network; Couplings; Eigenvalues and eigenfunctions; Kalman filters; Laplace equations; Linear systems; Observers; Symmetric matrices;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), 2014 International Joint Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-6627-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2014.6889465
  • Filename
    6889465