Premium
Stochastic model predictive control for tracking linear systems
Author(s) -
D' Jorge Agustina,
Santoro Bruno F.,
Anderson Alejandro,
González Alejandro H.,
Ferramosca Antonio
Publication year - 2019
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2501
Subject(s) - setpoint , control theory (sociology) , model predictive control , invariant (physics) , controller (irrigation) , tracking (education) , computer science , exponential stability , stability (learning theory) , mathematics , control (management) , artificial intelligence , nonlinear system , machine learning , physics , quantum mechanics , agronomy , mathematical physics , biology , psychology , pedagogy
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.