• DocumentCode
    2819386
  • Title

    Fast implementation of predictive controllers using SM approximation methodologies

  • Author

    Canale, M. ; Fagiano, L. ; Milanese, M.

  • Author_Institution
    Politecnico di Torino, Turin
  • fYear
    2007
  • fDate
    12-14 Dec. 2007
  • Firstpage
    1361
  • Lastpage
    1367
  • Abstract
    Set membership function estimation methodologies are employed in the approximation of a given predictive control law. This is obtained via the evaluation of an approximating function with a desired level of accuracy, fulfilling input constraints and whose computational time is independent on the MPC control horizon. The effects of employing the approximated control law can be treated as an additive perturbation acting on the system. Sufficient conditions are obtained for the approximating function to guarantee closed loop stability, state constraint satisfaction and limited performance degradation, in terms of distance between the nominal and perturbed state trajectories. Then, as the control computation is simply reduced to the evaluation of a static non linear function, the computational time is significantly reduced leading to a fast implementation of the given predictive controller.
  • Keywords
    closed loop systems; function approximation; predictive control; set theory; stability; MPC control horizon; SM approximation methodologies; closed loop stability; model predictive control law; set membership function estimation; state constraint satisfaction; Control systems; Neural networks; Nonlinear control systems; Predictive control; Predictive models; Samarium; Sampling methods; Stability; State-space methods; Time invariant systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control, 2007 46th IEEE Conference on
  • Conference_Location
    New Orleans, LA
  • ISSN
    0191-2216
  • Print_ISBN
    978-1-4244-1497-0
  • Electronic_ISBN
    0191-2216
  • Type

    conf

  • DOI
    10.1109/CDC.2007.4434307
  • Filename
    4434307