Worst-Case Identification from Closed-Loop Time Series

This paper studies identification of linear shift-invariant systems from closed-loop time series. Identification (or modelling) error is measured here by distance functions which lead to the weakest convergence notions for systems such that closed-loop stability, in the sense of BIBO stability, is a robust property. Thus the identification methodology developed here is compatible with the requirements of robust control design under l^∞ -stable co-prime factor uncertainty. Worst-case identification error bounds in several distance functions are included.