INVESTIGADORES
GONZALEZ Alejandro Hernan
artículos
Título:
Stochastic Model Predictive Control for tracking linear systems
Autor/es:
D'JORGE, AGUSTINA; SANTORO, BRUNO; ANDERSON, ALEJANDRO; GONZÁLEZ, ALEJANDRO HERNÁN; FERRAMOSCA, ANTONIO
Revista:
Optimal Control Application and Methods
Editorial:
John Wiley & Sons Ltd
Referencias:
Año: 2019
ISSN:
1099-1514
Resumen:
This note presents a stochastic formulation of the MPC for Tracking (MPCT). The proposal controller ensures constraints satisfaction in probability, and maintains the main features of the MPCT, that are feasibility for any changing setpoints and enlarged domain of attraction, even larger than the one delivered by, thanks to the use of the artificial reference and the relaxed terminal constraint. Asymptotic stability (in probability) of the closed-loop system, to theminimal robust positively invariant (RPI) set centered on the desired setpoint, is guaranteed.Simulations on a DC-DC converter show the benefits and the properties ofthe proposal.
