Structure Detection of Wiener–Hammerstein Systems With Process Noise

Identification of nonlinear block-oriented models has been extensively studied. The presence of the process noise and more precisely its location in the block-oriented model essentially influence the development of a consistent identification algorithm. This paper is proposed with the aim to localize the process noise in the block-oriented model for accurate nonlinear modeling. To this end, the response of a Wiener-Hammerstein system is theoretically analyzed, and the disturbance component in the output, caused by the process noise preceding the static nonlinearity, is shown to be dependent on the input signal. Inspired by such a theoretical observation, a simple and new protocol is developed to determine the location of the process noise with respect to the static nonlinearity using an input signal that is periodic but nonstationary within one period. In addition, the proposed technique is promising to detect the type of certain static nonlinearity (e.g., dead zone and saturation). Finally, it is validated on a simulated example and a real-life benchmark.