Highly Efficient Identification Methods for Dual-Rate Hammerstein Systems

Abstract

This brief concerns parameter identification for a dual-rate Hammerstein CARMA system. By combining the polynomial transformation technique and the hierarchical identification principle, this brief transforms a dual-rate nonlinear Hammerstein CARMA system into a bilinear dual-rate identification model, and presents a hierarchical least squares algorithm to estimate the parameter vectors of the bilinear dual-rate identification model. Moreover, by using the key term separation principle, this brief transforms the dual-rate nonlinear Hammerstein CARMA system into a linear dual-rate identification model, and presents a key term separation based least squares algorithm to estimate the parameter vector of the linear dual-rate identification model. The two proposed methods possess higher computational efficiency compared with the previous over-parameterization least squares method in which many redundant parameters need estimating. The simulation results show the effectiveness of the two proposed algorithms.