Identification of nonlinear dynamic systems as a composition of local linear parametric or state-space models

A technique for the identification of nonlinear systems as a composition of multivariable (MIMO) state-space local models is presented. First, fuzzy clustering with adaptive distance measure is applied to the Hankel matrix in order to obtain a partition of the data into fuzzy subsets which can be accurately approximated by local linear models. Then, after weighting the data by the membership degrees computed by fuzzy clustering, standard subspace identification algorithms can be applied to obtain the parameterization in terms of system matrices. The developed technique is applied to the identification of nonlinear pressure dynamics.