A Stochastic Approach to Linear Estimation in H∞

This paper examines the problem of system identification from frequency response data. Recent approaches to this problem, known collectively as ‘Estimation in H ! ’, involve deterministic descriptions of noise corruptions to the data. In order to provide ‘worst-case’ convergence with respect to these deterministic noise descriptions, non-linear data algorithms are required. In contrast, this paper examines ‘worst-case’ estimation in H ! when the disturbances are subject to mild stochastic assumptions and linearity in the data algorithms is employed. Issues of convergence, error bounds, and model order selection are considered. ! 1998 Elsevier Science Ltd. All rights reserved.