Computational burden reduction in Set-membership Hammerstein system identification

Hammerstein system identification from measurements affected by bounded noise is considered in the paper. First, we show that the computation of tight parameter bounds requires the solution to nonconvex optimization problems where the number of decision variables increases with the length of the experimental data sequence. Then, in order to reduce the computational burden of the identification problem, we propose a procedure to relax the previously formulated problem to a set of polynomial optimization problems where the number of variables does not depend on the measurements sequence size. Advantages of the presented approach with respect to previously published results are discussed.