Measurement noise error bounds for eigensystem realization algorithm

Author

Akers, James C. ; Bernstein, Dennis S.

Author_Institution

Dept. of Aerosp. Eng., Michigan Univ., Ann Arbor, MI, USA

Volume

4

fYear

1995

fDate

21-23 Jun 1995

Firstpage

2566

Abstract

This paper studies the identification of linear discrete-time time-invariant finite-dimensional systems using Markov parameters. It is shown that the eigensystem realisation algorithm (ERA) and Ho-Kalman realizations are special cases of a more general construction for obtaining minimal realizations from decompositions of the Markov block Hankel matrix. An upper bound for the error between the system and the reduced-order model formed from truncation of the ERA realization of the noisy Markov block Handel matrix is given. Finally, numerical computation of this new upper bound are given to illustrate these results

Keywords

Hankel matrices; Markov processes; discrete time systems; eigenvalues and eigenfunctions; identification; linear systems; multidimensional systems; reduced order systems; state-space methods; Markov block Hankel matrix; Markov parameters; eigensystem; finite-dimensional systems; identification; linear discrete-time systems; measurement noise error bounds; reduced-order model; state space; time-invariant systems; upper bound; Aerodynamics; Matrix decomposition; Noise level; Noise measurement; Noise reduction; Numerical simulation; Oscillators; Reduced order systems; Time domain analysis; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, Proceedings of the 1995

Conference_Location

Seattle, WA

Print_ISBN

0-7803-2445-5

Type

conf

DOI

10.1109/ACC.1995.532311

Filename

532311