Low-Complexity Polytopic Invariant Sets for Linear Systems Subject to Norm-Bounded Uncertainty

We propose a novel algorithm to compute low-complexity polytopic robust control invariant (RCI) sets, along with the corresponding state-feedback gain, for linear discrete-time systems subject to norm-bounded uncertainty, additive disturbances and state/input constraints. Using a slack variable appr...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 60; no. 5; pp. 1416 - 1421
Main Authors Tahir, Furqan, Jaimoukha, Imad M.
Format Journal Article
LanguageEnglish
Published IEEE 01.05.2015
Subjects
Additives
Approximation algorithms
Approximation methods
Linear matrix inequalities
Optimization
Silicon
Uncertainty
robust control invariant set
slack variables
Norm-bounded uncertainty
optimization
S-procedure
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Summary:We propose a novel algorithm to compute low-complexity polytopic robust control invariant (RCI) sets, along with the corresponding state-feedback gain, for linear discrete-time systems subject to norm-bounded uncertainty, additive disturbances and state/input constraints. Using a slack variable approach, we propose new results to transform the original nonlinear problem into a convex/LMI problem whilst introducing only minor conservatism in the formulation. Through numerical examples, we illustrate that the proposed algorithm can yield improved maximal/minimal volume RCI set approximations in comparison with the schemes given in the literature.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2014.2352692