Hierarchical Least Squares Identification for Hammerstein Nonlinear Controlled Autoregressive Systems

This paper considers the parametric identification problems of a Hammerstein nonlinear system which consists of a static nonlinear block followed by a linear dynamic subsystem. A hierarchical least squares algorithm is developed by using the hierarchical identification principle, which decomposes a nonlinear system into several subsystems with smaller dimensions and fewer variables and estimates the parameters of each subsystem, respectively. The performance analysis indicates that the parameter estimates given by the proposed algorithm converge to their true values and the proposed algorithm requires higher computational efficiencies compared with the recursive least squares algorithm.