A unified framework for the identification of a general class of multivariable nonlinear block-structured systems

This article addresses the parametric identification of block-structured nonlinear systems in a general form, characterized by the feedback interconnection of a multivariable linear system and a static multivariate nonlinear map. We assume that both the input and the output collected data are affected by bounded noise, which casts the problem in the context of set-membership (SM) errors-in-variables identification. We introduce a single-stage SM identification algorithm for the computation of the parameter uncertainty intervals. The proposed solution exploits the formulation of a suitable optimization problem solved through convex relaxation techniques. Numerical simulations and an experimental test show the effectiveness of the proposed approach.