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Output feedback model predictive control of uncertain norm‐bounded linear systems
Author(s) -
Famularo D.,
Franzè G.
Publication year - 2011
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.1629
Subject(s) - control theory (sociology) , model predictive control , bounded function , norm (philosophy) , mathematics , linear matrix inequality , linear system , observer (physics) , full state feedback , mathematical optimization , state (computer science) , bilinear interpolation , computer science , control (management) , algorithm , mathematical analysis , physics , quantum mechanics , artificial intelligence , political science , law , statistics
Abstract A constrained output feedback model predictive control (MPC) scheme for uncertain Norm‐Bounded discrete‐time linear systems is presented. This scheme extends recent results achieved by the authors under full‐state availability to the more interesting case of incomplete and noisy state information. The design procedure consists of an off‐line step where a state feedback and an asymptotic observer (dynamic primal controller) are designed via bilinear matrix inequalities and used to robustly stabilize a suitably augmented state plant. The on‐line moving horizon procedure adds N free control moves to the action of the primal controller which are computed by solving a linear matrix inequality optimization problem whose numerical complexity grows up only linearly with the control horizon N . The effectiveness of the proposed MPC strategy is illustrated by a numerical example. Copyright © 2010 John Wiley & Sons, Ltd.