• DocumentCode
    57662
  • Title

    Controllability and Observability of Boolean Control Networks With Time-Variant Delays in States

  • Author

    Lijun Zhang ; Kuize Zhang

  • Author_Institution
    Coll. of Autom., Harbin Eng. Univ., Harbin, China
  • Volume
    24
  • Issue
    9
  • fYear
    2013
  • fDate
    Sept. 2013
  • Firstpage
    1478
  • Lastpage
    1484
  • Abstract
    This brief investigates the controllability and observability of Boolean control networks with (not necessarily bounded) time-variant delays in states. After a brief introduction to converting a Boolean control network to an equivalent discrete-time bilinear dynamical system via the semi-tensor product of matrices, the system is split into a finite number of subsystems (constructed forest) with no time delays by using the idea of splitting time that is proposed in this brief. Then, the controllability and observability of the system are investigated by verifying any so-called controllability constructed path and any so-called observability constructed paths in the above forest, respectively, which generalize some recent relevant results. Matrix test criteria for the controllability and observability are given. The corresponding control design algorithms based on the controllability theorems are given. We also show that the computing complexity of our algorithm is much less than that of the existing algorithms.
  • Keywords
    bilinear systems; computational complexity; control system synthesis; controllability; delays; discrete time systems; matrix algebra; observability; tensors; Boolean control networks; computing complexity; control design algorithms; controllability constructed path; controllability theorems; discrete-time bilinear dynamical system; matrix test criteria; observability constructed paths; semitensor matrix product; splitting time; time-variant delays; Boolean control network; controllability; observability; semi-tensor product of matrices; time delay;
  • fLanguage
    English
  • Journal_Title
    Neural Networks and Learning Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    2162-237X
  • Type

    jour

  • DOI
    10.1109/TNNLS.2013.2246187
  • Filename
    6515369