• DocumentCode
    183750
  • Title

    Output feedback economic model predictive control of parabolic PDE systems

  • Author

    Liangfeng Lao ; Ellis, Matthew ; Christofides, Panagiotis D.

  • Author_Institution
    Dept. of Chem. & Biomol. Eng., Univ. of California, Los Angeles, Los Angeles, CA, USA
  • fYear
    2014
  • fDate
    4-6 June 2014
  • Firstpage
    1655
  • Lastpage
    1660
  • Abstract
    We proposed an economic model predictive control (EMPC) system for parabolic partial differential equation (PDE) systems in [17]. The EMPC system assumed the knowledge of the complete state spatial profile at each sampling period. From a practical point of view, measurements of the state variables are available only at a finite number of spatial positions. To address this practical consideration, an output feedback EMPC system that accounts for both manipulated input and state constraints is developed for a quasi-linear parabolic PDE system. The EMPC system is applied to a non-isothermal tubular reactor. Two EMPC systems, each utilizing a different number of measurement sensors and formulated with various degrees of accuracy (i.e., number of modes retained from the infinite-dimensional model), are presented and compared on the basis of model accuracy, and input and state constraint satisfaction.
  • Keywords
    constraint satisfaction problems; economics; feedback; parabolic equations; partial differential equations; predictive control; measurement sensors; nonisothermal tubular reactor; output feedback EMPC system; output feedback economic model predictive control; parabolic PDE systems; parabolic partial differential equation systems; quasilinear parabolic PDE system; state constraint satisfaction; state variable measurements; Accuracy; Economics; Eigenvalues and eigenfunctions; Inductors; Mathematical model; Output feedback; Vectors; Distributed parameter systems; Output feedback; Predictive control for nonlinear systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference (ACC), 2014
  • Conference_Location
    Portland, OR
  • ISSN
    0743-1619
  • Print_ISBN
    978-1-4799-3272-6
  • Type

    conf

  • DOI
    10.1109/ACC.2014.6858762
  • Filename
    6858762