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

Massoud Hemmasian Ettefagh, Jose De Dona, Farzad Towhidkhah and Mahyar Naraghi

International Journal of Systems Science, 2021, vol. 52, issue 16, 3539-3555

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.

Date: 2021
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DOI: 10.1080/00207721.2021.1933252

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