INTEC   05402
INSTITUTO DE DESARROLLO TECNOLOGICO PARA LA INDUSTRIA QUIMICA
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
Robust and Stochastic MPC for Tracking: A Performance Comparison
Autor/es:
AGUSTINA D'JORGE; ANTONIO FERRAMOSCA; ALEJANDRO ANDERSON; ALEJANDRO HERNAN GONZALEZ
Lugar:
Buenos Aires
Reunión:
Congreso; 2018 Argentine Conference on Automatic Control (AADECA); 2018
Resumen:
Model Predictive Control for Tracking (MPCT) is an advanced control strategy that allows to change the reference point without losing feasibility. Furthermore, the formulation of the MPCT allows to enlarge the domain of attraction conceding it higher controllability. These advantages make it a strategy with wide variety of applications. The goal of this work is to give a comparison between the existing formulations of Robust MPCT and stochastic MPCT. An illustrative example shows the properties of theses controllers.