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Adaptive-pole selection in the Laguerre parametrisation of model predictive control to achieve high performance

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  • Massoud Hemmasian Ettefagh
  • Jose De Dona
  • Farzad Towhidkhah
  • Mahyar Naraghi

Abstract

In this paper, we study an adaptive method to select online the pole value for a Laguerre scheme in Model Predictive Control (MPC) that yields high performance. It has been observed that, while still using a small numbers of decision variables, the location of the pole affects the closed-loop behaviour significantly. In the present paper, an adaptive algorithm is developed to systematically improve the closed-loop performance of the system as well as the volume of the feasible region and robust feasible region in the case of using a small numbers of decision variables. In order to do this, a method to select a pole value that yields high performance for the initial condition of the system is proposed. The method generates a lookup table of the high-performance pole value obtained through off-line computations. Then, the table is used to assign the pole in the online process. Closed-loop stability for the scheme is established using sub-optimality arguments. Simulations illustrate the suggested method's effectiveness to achieve a balance between performance, optimality, and computational load.

Suggested Citation

  • Massoud Hemmasian Ettefagh & Jose De Dona & Farzad Towhidkhah & Mahyar Naraghi, 2021. "Adaptive-pole selection in the Laguerre parametrisation of model predictive control to achieve high performance," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(16), pages 3539-3555, December.
    • Handle: RePEc:taf:tsysxx:v:52:y:2021:i:16:p:3539-3555
    DOI: 10.1080/00207721.2021.1933252
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