@incollection{eprints2448,
       publisher = {IFAC},
           month = {July},
            note = {16th IFAC Symposium on System Identification held in Brussels (Belgium), 11-13 July 2012},
            year = {2012},
          volume = {16},
           title = {Input-Output LPV Model identification with guaranteed quadratic stability},
       booktitle = {16th IFAC Symposium on System Identification},
          author = {Vito Cerone and Dario Piga and Diego Regruto and Roland T{\'o}th},
           pages = {1767--1772},
             url = {http://eprints.imtlucca.it/2448/},
        abstract = {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.},
        keywords = {LPV system; quadratic stability; polynomial optimization; convex relaxation}
}