Hierarchical Stochastic Gradient Algorithm and its Performance Analysis for a Class of Bilinear-in-Parameter Systems

Abstract

This paper considers the parameter identification for a special class of nonlinear systems, i.e., bilinear-in-parameter systems. Based on the hierarchical identification principle, a hierarchical stochastic gradient (HSG) estimation algorithm is presented. The basic idea is to decompose a bilinear-in-parameter system into two subsystems and to derive the HSG identification algorithm for estimating the system parameters by replacing the unknown variables in the information vectors with their estimates obtained at the previous time. The convergence analysis of the proposed algorithm indicates that the parameter estimation errors converge to zero under persistent excitation conditions. The simulation results show that the proposed algorithm is effective.