PROJECT TITLE :
Low-Complexity Polytopic Invariant Sets for Linear Systems Subject to Norm-Bounded Uncertainty
We have a tendency to propose a novel algorithm to compute low-complexity polytopic strong control invariant (RCI) sets, together 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 have a tendency to propose new results to remodel the original nonlinear drawback into a convex/LMI drawback whilst introducing solely minor conservatism in the formulation. Through numerical examples, we illustrate that the proposed algorithm will yield improved maximal/minimal volume RCI set approximations as compared with the schemes given in the literature.
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