TY  - JOUR
AU  - D' Jorge, Agustina
AU  - Santoro, Bruno F.
AU  - Anderson, Alejandro
AU  - González, Alejandro H.
AU  - Ferramosca, Antonio
C8  - oca.2501
TI  - Stochastic model predictive control for tracking linear systems
JO  - Optimal Control Applications and Methods
JA  - Optim Control Appl Meth
VL  - 41
IS  - 1
SN  - 0143-2087
UR  - https://doi.org/10.1002/oca.2501
DO  - https://doi.org/10.1002/oca.2501
SP  - 65
EP  - 83
KW  - model predictive control
KW  - probabilistic constraint
KW  - stochastic control
KW  - tracking
PY  - 2020
AB  - Summary This note presents a stochastic formulation of the model predictive control for tracking (MPCT), based on the results of the work of Lorenzen et al. The proposed 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 Lorenzen et al, thanks to the use of artificial references and relaxed terminal constraints. The asymptotic stability (in probability) of the minimal robust positively invariant set centered on the desired setpoint is guaranteed. Simulations on a DC-DC converter show the benefits and the properties of the proposal.
ER  -