Worst-case analysis of identification-BIBO robustness for closed-loop data

Author

Partington, J.R. ; Kila, P. M M

Author_Institution

Sch. of Math., Leeds Univ., UK

Volume

39

Issue

10

fYear

1994

fDate

10/1/1994 12:00:00 AM

Firstpage

2171

Lastpage

2176

Abstract

This paper deals with the worst-case analysis of identification of linear shift-invariant (possibly) infinite-dimensional systems. A necessary and sufficient input richness condition for the existence of robustly convergent identification algorithms in l¹ is given. A closed-loop identification setting is studied to cover both stable and unstable (but BIBO stabilizable) systems. Identification (or modeling) error is then measured 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. Worst-case modeling error bounds in several distance functions are included

Keywords

closed loop systems; convergence; identification; linear systems; multidimensional systems; stability; transient response; BIBO robustness; BIBO stabilizable systems; closed-loop data; closed-loop identification; closed-loop stability; distance functions; identification; identification error; linear shift-invariant infinite-dimensional systems; necessary and sufficient input richness condition; robustly convergent identification algorithms; weakest convergence notions; worst-case analysis; worst-case modeling error bounds; Autoregressive processes; Convergence; Feedback; Hilbert space; Linear systems; Optimal control; Particle measurements; Robust stability; Robustness; Size measurement;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.328804

Filename

328804