• DocumentCode
    184328
  • Title

    Extremum seeking-based iterative learning linear MPC

  • Author

    Benosman, M. ; Di Cairano, S. ; Weiss, A.

  • Author_Institution
    Mitsubishi Electr. Res. Labs., Cambridge, MA, USA
  • fYear
    2014
  • fDate
    8-10 Oct. 2014
  • Firstpage
    1849
  • Lastpage
    1854
  • Abstract
    In this work we study the problem of adaptive MPC for linear time-invariant uncertain models. We assume linear models with parametric uncertainties, and propose an iterative multi-variable extremum seeking (MES)-based learning MPC algorithm to learn online the uncertain parameters and update the MPC model. We show the effectiveness of this algorithm on a DC servo motor control example.
  • Keywords
    adaptive control; iterative learning control; linear systems; machine control; predictive control; servomechanisms; time-varying systems; uncertain systems; DC servo motor control; adaptive MPC; extremum seeking-based iterative learning; iterative multivariable extremum seeking-based learning; linear MPC; linear time-invariant uncertain models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Applications (CCA), 2014 IEEE Conference on
  • Conference_Location
    Juan Les Antibes
  • Type

    conf

  • DOI
    10.1109/CCA.2014.6981582
  • Filename
    6981582
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=184328