Analyzing Iterations in Identification with Application to Nonparametric $mathcal{H}_infty$-Norm Estimation

In the last decades, many iterative approaches in the field of system identification for control have been proposed. Many successful implementations have been reported, despite the lack of a solid analysis with respect to the convergence and value of these iterations. The aim of this paper is to present a thorough analysis of a specific iterative algorithm that involves nonparametric H∞-norm estimation. The pursued approach involves a novel frequency domain approach that appropriately deals with additive stochastic disturbances and input normalization. The results of the novel convergence analysis are twofold: i) the presence of additive disturbances introduces a bias in the estimation procedure, and ii) the iterative procedure can be interpreted as experiment design for H∞-norm estimation, revealing the value of iterations and limits of accuracy in terms of the Fisher information matrix. The results are confirmed by means of a simulation example.