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Model predictive tracking control for a linear system under time‐varying input constraints
Author(s) -
Wada N.,
Tomosugi H.,
Saeki M.
Publication year - 2013
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.2806
Subject(s) - control theory (sociology) , model predictive control , linear matrix inequality , dual (grammatical number) , limit (mathematics) , computer science , controller (irrigation) , actuator , stability (learning theory) , control (management) , optimization problem , position (finance) , linear system , law , mathematics , mathematical optimization , algorithm , artificial intelligence , art , mathematical analysis , literature , machine learning , political science , agronomy , biology , finance , economics
SUMMARY In this paper, we propose a tracking control law for a linear dynamical system under time‐varying input constraints. The proposed control law consists of a dual‐mode model predictive control (MPC) law and a target recalculation mechanism. As the terminal controller of the dual‐mode MPC, we propose a saturation‐level‐dependent gain‐scheduled feedback control law that ensures closed‐loop stability against arbitrary change of the position limit of the actuators. We also present conditions that guarantee feasibility and stability of the control algorithm under time‐varying input constraints. The control algorithm is reduced to an online optimization problem under linear matrix inequality constraints. Copyright © 2012 John Wiley & Sons, Ltd.