Identification of the deterministic part of MIMO state space models given in innovations form from input-output data

doi:10.1016/0005-1098(94)90229-1

Automatica

Volume 30, Issue 1, January 1994, Pages 61-74

https://doi.org/10.1016/0005-1098(94)90229-1 Get rights and content

Abstract

In this paper we describe two algorithms to identify a linear, time-invariant, finite dimensional state space model from input-output data. The system to be identified is assumed to be excited by a measurable input and an unknown process noise and the measurements are disturbed by unknown measurement noise. Both noise sequences are discrete zero-mean white noise. The first algorithm gives consistent estimates only for the case where the input also is zero-mean white noise, while the same result is obtained with the second algorithm without this constraint. For the special case where the input signal is discrete zero-mean white noise, it is explicitly shown that this second algorithm is a special case of the recently developed Multivariable Output-Error State Space (moesp) class of algorithms based on instrumental variables. The usefulness of the presented schemes is highlighted in a realistic simulation study.

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^☆: This paper was not presented at any IFAC meeting. This paper was recommended for publication in revised form by the Guest Editors.

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Identification of the deterministic part of MIMO state space models given in innovations form from input-output data☆

Abstract

Markovian representation of stochastic processes by canonical variables

Siam J. Contr.

Optimal Filtering

NASA's advanced control law program for the F-8 digital fly-by-wire aircraft

IEEE Transactions on Automatic Control

Matrix Computations

Canonical variate analysis in identification, filtering and adaptive control

System Identification: Theory for the User

An exact subspace algorithm for the identification of combined deterministic-stochastic systems