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    Time-delay estimation for nonlinear systems with piecewise-constant input

    199825_199825.pdf (314.7Kb)
    Access Status
    Open access
    Authors
    Chai, Q.
    Loxton, Ryan
    Teo, Kok Lay
    Yang, C.
    Date
    2013
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Chai, Q. and Loxton, R. and Teo, K.L. and Yang, C. 2013. Time-delay estimation for nonlinear systems with piecewise-constant input. Applied Mathematics and Computations. 219 (7): pp. 9543-9560.
    Source Title
    Applied Mathematics and Computations
    DOI
    10.1016/j.amc.2013.03.015
    ISSN
    0096-3003
    School
    Department of Mathematics and Statistics
    Remarks

    NOTICE: This is the author’s version of a work that was accepted for publication in Applied Mathematics and Computations. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published Applied Mathematics and Computations, Vol. 219, Issue 7. (2013). doi: 10.1016/j.amc.2013.03.015

    URI
    http://hdl.handle.net/20.500.11937/22951
    Collection
    • Curtin Research Publications
    Abstract

    We consider a general nonlinear time-delay system in which the input signal is piecewise-constant. Such systems arise in a wide range of industrial applications, including evaporation and purification processes and chromatography. We assume that the time-delays—one involving the state variables and the other involving the input variables—are unknown and need to be estimated using experimental data. We formulate the problem of estimating the unknown delays as a nonlinear optimization problem in which the cost function measures the least-squares error between predicted and measured system output. The main difficulty with this problem is that the delays are decision variables to be optimized, rather than fixed values. Thus, conventional optimization techniques are not directly applicable. We propose a new computational approach based on a novel algorithm for computing the cost function’s gradient. We then apply this approach to estimate the time-delays in two industrial chemical processes: a zinc sulphate purification process and a sodium aluminate evaporation process.

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