Open AccessRobust linear matrix inequality‐based model predictive control with recursive estimation of the uncertainty polytope
Author(s) -
Cavalca Mariana Santos Matos,
Galvão Roberto Kawakami Harrop,
Yoneyama Takashi
Publication year - 2013
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2012.0586
Subject(s) - polytope , model predictive control , bounded function , mathematics , control theory (sociology) , linear matrix inequality , mathematical optimization , stability (learning theory) , robustness (evolution) , robust control , extension (predicate logic) , matrix (chemical analysis) , function (biology) , computer science , control (management) , control system , artificial intelligence , engineering , discrete mathematics , materials science , mathematical analysis , chemistry , composite material , biology , biochemistry , machine learning , evolutionary biology , programming language , electrical engineering , gene
The present work is concerned with the recursive estimation of the uncertainty polytope in a robust model predictive control (RMPC) framework. For this purpose, the unknown but bounded error method is employed to update the uncertainty polytope on the basis of sensor measurements at each sampling period. The recursive feasibility and asymptotic stability properties of the proposed approach are demonstrated as an extension of previous results concerning the RMPC formulation. For illustration, a simulated example involving an angular positioning system is presented. The results show that the proposed scheme provides a performance improvement, as indicated by the resulting cost function values.