Asymptotically optimal orthonormal basis functions for LPV system identification
Automatica (Journal of IFAC)
On the quadratic stability of descriptor systems with uncertainties in the derivative matrix
International Journal of Systems Science
Brief paper: Robust controllability and observability degrees of polynomially uncertain systems
Automatica (Journal of IFAC)
IEEE Transactions on Fuzzy Systems
Exploiting sparsity in the sum-of-squares approximations to robust semidefinite programs
ACC'09 Proceedings of the 2009 conference on American Control Conference
Parameter-dependent Slack variable approach for positivity check of polynomials over hyper-rectangle
ACC'09 Proceedings of the 2009 conference on American Control Conference
Journal of Symbolic Computation
An Exact Duality Theory for Semidefinite Programming Based on Sums of Squares
Mathematics of Operations Research
Welfare-maximizing correlated equilibria using Kantorovich polynomials with sparsity
Journal of Global Optimization
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We consider robust semi-definite programs which depend polynomially or rationally on some uncertain parameter that is only known to be contained in a set with a polynomial matrix inequality description. On the basis of matrix sum-of-squares decompositions, we suggest a systematic procedure to construct a family of linear matrix inequality relaxations for computing upper bounds on the optimal value of the corresponding robust counterpart. With a novel matrix-version of Putinar's sum-of-squares representation for positive polynomials on compact semi-algebraic sets, we prove asymptotic exactness of the relaxation family under a suitable constraint qualification. If the uncertainty region is a compact polytope, we provide a new duality proof for the validity of Putinar's constraint qualification with an a priori degree bound on the polynomial certificates. Finally, we point out the consequences of our results for constructing relaxations based on the so-called full-block S-procedure, which allows to apply recently developed tests in order to computationally verify the exactness of possibly small-sized relaxations.