TY - GEN
T1 - Experiment designs to minimize input peak and crest factor in MIMO system identification
AU - Häggblom, Kurt Erik
N1 - rt.
Date Added to IEEE Xplore: 05 December 2019
Post-print, 24 mån embargo, publ. Rights
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Har kontaktat kurt-erik.haggblom@abo.fi den 27.2.2020/LN
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PY - 2019
Y1 - 2019
N2 - The quality of the data for system identification is of utmost of importance. Ideally, the output data should satisfy requirements regarding variance, and in the case of multiple-input multiple-output (MIMO) systems, correlation between the outputs. Usually, it is desired to achieve this by input perturbations with peak values as small as possible. A related measure is the crest factor, which is a measure of the (inverse) power of an input perturbation with given peak value. In this paper, the effect of minimizing the input peak and the crest factor, subject to desired output variances/covariances, is studied for various types of perturbation signals. The design procedure is completely data based; data are obtained by one or more preliminary experiments with the system to be identified. A model of an ill-conditioned distillation column is used for illustration.
AB - The quality of the data for system identification is of utmost of importance. Ideally, the output data should satisfy requirements regarding variance, and in the case of multiple-input multiple-output (MIMO) systems, correlation between the outputs. Usually, it is desired to achieve this by input perturbations with peak values as small as possible. A related measure is the crest factor, which is a measure of the (inverse) power of an input perturbation with given peak value. In this paper, the effect of minimizing the input peak and the crest factor, subject to desired output variances/covariances, is studied for various types of perturbation signals. The design procedure is completely data based; data are obtained by one or more preliminary experiments with the system to be identified. A model of an ill-conditioned distillation column is used for illustration.
KW - data-based design
KW - experiment design
KW - ill-conditioned systems
KW - multivariable systems
KW - system identification
KW - data-based design
KW - experiment design
KW - ill-conditioned systems
KW - multivariable systems
KW - system identification
KW - data-based design
KW - experiment design
KW - ill-conditioned systems
KW - multivariable systems
KW - system identification
U2 - 10.1109/CCTA.2019.8920499
DO - 10.1109/CCTA.2019.8920499
M3 - Konferensbidrag
SN - 978-1-7281-2767-5
SP - 499
EP - 504
BT - IEEE CCTA 2019 : 3rd IEEE conference on control technology and applications : August 19-21, 2019, City University of Hong Kong, Hong Kong, China
PB - IEEE
T2 - IEEE Conference on Control Technology and Applications
Y2 - 19 August 2019 through 21 August 2019
ER -