Identification of a discrete-time dynamical system

Author

Pachter, M. ; Reynolds, Odell R.

Author_Institution

Inst. of Technol., Wright-Patterson AFB, OH, USA

Volume

36

Issue

1

fYear

2000

fDate

1/1/2000 12:00:00 AM

Firstpage

212

Lastpage

225

Abstract

A novel generalized minimum variance (GMV) system identification algorithm is developed, and its performance is gauged against the established generalized least squares (GLS) estimation algorithm. The emphasis of the proposed GMV algorithm is on the rigorous treatment of measurement noise for dynamical system identification. A careful analysis of the measurement situation on hand yields a novel fixed-point calculation-based parameter estimation algorithm. The novel and established algorithms are compared in carefully performed and reproducible experiments which include measurement noise. Differences are apparent under small (measurement) sample operation, whereas under sufficient excitation, the algorithms produce statistically similar results

Keywords

Gaussian noise; covariance matrices; discrete time systems; linear systems; parameter estimation; transfer function matrices; Gaussian noise; convergence; covariance matrix; discrete-time dynamical system; dynamical system identification; fixed-point calculation-based; generalized minimum variance system; identification algorithm; linear control system; measurement noise; parameter estimation algorithm; sampling rate effects; second-order system; transfer function; Algorithm design and analysis; Control systems; Least squares approximation; Military computing; Noise measurement; Parameter estimation; Performance evaluation; Sampling methods; System identification; Transfer functions;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.826323

Filename

826323