• DocumentCode
    631850
  • Title

    PID gain scheduling by parametric model predictive control

  • Author

    Nguyen, Minh H.-T ; Kok Kiong Tan ; Chek Sing Teo

  • Author_Institution
    SIMTech-NUS Joint Lab. (Precision Motion Syst.), Nat. Univ. of Singapore, Singapore, Singapore
  • fYear
    2013
  • fDate
    9-12 July 2013
  • Firstpage
    944
  • Lastpage
    948
  • Abstract
    This paper considers the problem of augmenting the PID structure with the MPC functionality of constraint handling and optimization. Firstly, we review the MPC framework which can be built from a model and a linear feedback gain. This linear gain can be any preexisting multi-loop PID design in the unconstrained case, or based on the two stabilizing PI/PID design for multivariable systems we introduce here. The resulting controller is a feedforward PID mapping, a straightforward form without the need of tuning PID to fit an optimal input. Secondly, the parametric solution of MPC further suggests a PID network implementation by utilizing gain scheduling schemes available in industry.
  • Keywords
    control system synthesis; multivariable systems; optimisation; predictive control; stability; three-term control; MPC framework; PID gain scheduling scheme; constraint handling; feedforward PID mapping; linear feedback gain; multi-loop PID design; multivariable systems; optimization; parametric model predictive control; Feedforward neural networks; Optimal control; Output feedback; PD control; Predictive control; Robustness; Steady-state;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Intelligent Mechatronics (AIM), 2013 IEEE/ASME International Conference on
  • Conference_Location
    Wollongong, NSW
  • ISSN
    2159-6247
  • Print_ISBN
    978-1-4673-5319-9
  • Type

    conf

  • DOI
    10.1109/AIM.2013.6584215
  • Filename
    6584215