Two Nonlinear Optimization Methods for Black Box Identification Compared

In this paper, two nonlinear optimization methods for the identification of nonlinear systems are compared. Both methods estimate all the parameters of a polynomial nonlinear state-space model by means of a nonlinear least-squares optimization. While the first method does not estimate the states explicitly, the second estimates both states and parameters adding an extra constraint equation. Both methods are introduced and their similarities and differences are discussed utilizing simulation and experimental data. The unconstrained method appears to be faster and more memory efficient, while the constrained method is robust towards instabilities.status: publishe