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Design of Robust Predictive Control Laws Using Set Membership Identified Models
Author(s) -
Canale M.,
Fagiano L.,
Signorile M.C.
Publication year - 2013
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.560
Subject(s) - model predictive control , control theory (sociology) , nonlinear system , set (abstract data type) , identification (biology) , robust control , process (computing) , computer science , mathematics , control (management) , mathematical optimization , law , artificial intelligence , physics , botany , quantum mechanics , biology , programming language , operating system , political science
This paper investigates the robust design of nonlinear model predictive control (NMPC) laws that employ approximated models, derived directly from process input‐output data. In particular, a nonlinear set membership (NSM) identification technique is used to obtain a system model and a bound of the related uncertainty. The latter is used to carry out a robust control design, via a min‐max formulation of the optimal control problem underlying the NMPC methodology. A numerical example with a nonlinear oscillator shows the effectiveness of the proposed approach.