• DocumentCode
    1736
  • Title

    A New Identification Framework for Off-Line Computation of Moving-Horizon Observers

  • Author

    Alamir, Mazen

  • Author_Institution
    Control Syst. Dept., Univ. of Grenoble, St. Martin d´Hères, France
  • Volume
    58
  • Issue
    7
  • fYear
    2013
  • fDate
    Jul-13
  • Firstpage
    1877
  • Lastpage
    1882
  • Abstract
    In this technical note, a new nonlinear identification framework is proposed to address the issue of off-line computation of moving-horizon observer estimate. The proposed structure merges the advantages of nonlinear approximators with the efficient computation of constrained quadratic programming problems. A bound on the estimation error is proposed and the efficiency of the resulting scheme is illustrated using two state estimation examples.
  • Keywords
    approximation theory; observers; quadratic programming; constrained quadratic programming problems; estimation error; moving-horizon observers; nonlinear approximators; nonlinear identification framework; offline computation; state estimation examples; Approximation methods; Noise; Noise measurement; Observers; Vectors; Ecoli; nonlinear identification; nonlinear moving-horizon observers; off-line computation; state estimation;
  • fLanguage
    English
  • Journal_Title
    Automatic Control, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9286
  • Type

    jour

  • DOI
    10.1109/TAC.2013.2256016
  • Filename
    6490340
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1736