Low-Complexity Polytopic Invariant Sets for Linear Systems Subject to Norm-Bounded Uncertainty PROJECT TITLE :Low-Complexity Polytopic Invariant Sets for Linear Systems Subject to Norm-Bounded UncertaintyABSTRACT: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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest The Effect of Magnetic Field Distribution and Pole Array on the Vertical Levitation Force Properties of HTS Maglev Systems Fast target object map handover
