A data-centric system identification approach to input signal design for Hammerstein systems

This paper examines the design of input signals for identification of Hammerstein systems in a data-centric framework by addressing the optimal distribution of regressors. Data-centric estimation methods such as Model-on-Demand (MoD) generate local function approximations from a database of regressors at the current operating point. The data-centric input signal design formulation aims to develop sufficient support in the regressor space for the MoD estimator, while addressing time-domain constraints on the input and output signals. A numerical example is shown to highlight the benefit of proposed design over classical Pseudo Random Binary Sequence (PRBS), Multi Level Pseudo Random Sequence (MLPRS) and uniform random input designs.