%B 16th IFAC Symposium on System Identification
%R 10.3182/20120711-3-BE-2027.00242
%K LPV system; quadratic stability; polynomial optimization; convex relaxation
%A Vito Cerone
%A Dario Piga
%A Diego Regruto
%A Roland T?th
%L eprints2448
%D 2012
%X The problem of identifying linear parameter-varying (LPV) systems, a-priori known to be quadratically stable, is considered in the paper using an input-output model structure. To solve this problem, a novel constrained optimization-based algorithm is proposed which guarantees quadratic stability of the identified model. It is shown that this estimation objective corresponds to a nonconvex optimization problem, defined by a set of polynomial matrix inequalities (PMI), whose optimal solution can be approximated by means of suitable convex semidefinite relaxations. Applicability of such relaxation-based estimation approach in the presence of either stochastic or deterministic bounded noise is discussed. A simulation example is also given to demonstrate the effectiveness of the resulting identification method.
%O 16th IFAC Symposium on System Identification held in Brussels (Belgium), 11-13 July 2012
%V 16
%I IFAC
%P 1767-1772
%T Input-Output LPV Model identification with guaranteed quadratic stability