Instrumental variable methods for identifying partial differential equation models

Julien Schorsch; Hugues Garnier; Marion Gilson; Peter C. Young

doi:10.1080/00207179.2013.813690

Instrumental variable methods for identifying partial differential equation models

Julien Schorsch^*, Hugues Garnier, Marion Gilson, Peter C. Young

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

Abstract

This paper presents a refined instrumental variable method for identifying partial differential equation models of distributed parameter systems directly from discrete-time sampled input-output data. The proposed method is compared with conventional least-squares and other instrumental variable-based techniques. Monte Carlo simulation analysis results are presented to illustrate the effectiveness and superiority of the proposed method in the presence of additive output measurement noise and under different spatio-temporal sampling conditions.

Original language	English
Pages (from-to)	2325-2335
Number of pages	11
Journal	International Journal of Control
Volume	86
Issue number	12
DOIs	https://doi.org/10.1080/00207179.2013.813690
Publication status	Published - 1 Dec 2013

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10.1080/00207179.2013.813690

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AU - Young, Peter C.

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AB - This paper presents a refined instrumental variable method for identifying partial differential equation models of distributed parameter systems directly from discrete-time sampled input-output data. The proposed method is compared with conventional least-squares and other instrumental variable-based techniques. Monte Carlo simulation analysis results are presented to illustrate the effectiveness and superiority of the proposed method in the presence of additive output measurement noise and under different spatio-temporal sampling conditions.

KW - Continuous-time

KW - Distributed parameter system

KW - Instrumental variable

KW - Partial differential equation

KW - System identification

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JF - International Journal of Control

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Instrumental variable methods for identifying partial differential equation models

Abstract

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