Non-parametric identification of multivariable systems: a local rational modeling approach with application to a vibration isolation benchmark

R.J. Voorhoeve, A. van der Maas, T.A.J. Oomen

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
9 Downloads (Pure)

Abstract

Frequency response function (FRF) identification is often used as a basis for control systems design and as a starting point for subsequent parametric system identification. The aim of this paper is to develop a multiple-input multiple-output (MIMO) local parametric modeling approach for FRF identification of lightly damped mechanical systems with improved speed and accuracy. The proposed method is based on local rational models, which can efficiently handle the lightly-damped resonant dynamics. A key aspect herein is the freedom in the multivariable rational model parametrizations. Several choices for such multivariable rational model parametrizations are proposed and investigated. For systems with many inputs and outputs the required number of model parameters can rapidly increase, adversely affecting the performance of the local modeling approach. Therefore, low-order model structures are investigated. The structure of these low-order parametrizations leads to an undesired directionality in the identification problem. To address this, an iterative local rational modeling algorithm is proposed. As a special case recently developed SISO algorithms are recovered. The proposed approach is successfully demonstrated on simulations and on an active vibration isolation system benchmark, confirming good performance of the method using significantly less parameters compared with alternative approaches.

Original languageEnglish
Pages (from-to)129-152
Number of pages24
JournalMechanical Systems and Signal Processing
Volume105
DOIs
Publication statusPublished - 15 May 2018

Fingerprint

Multivariable systems
Identification (control systems)
Frequency response
Model structures
Systems analysis
Control systems

Keywords

  • Frequency response function
  • Local parametric modeling
  • Matrix fraction description
  • Non-parametric
  • System identification

Cite this

@article{2c7da9b3177e4522b00db91df45a805b,
title = "Non-parametric identification of multivariable systems: a local rational modeling approach with application to a vibration isolation benchmark",
abstract = "Frequency response function (FRF) identification is often used as a basis for control systems design and as a starting point for subsequent parametric system identification. The aim of this paper is to develop a multiple-input multiple-output (MIMO) local parametric modeling approach for FRF identification of lightly damped mechanical systems with improved speed and accuracy. The proposed method is based on local rational models, which can efficiently handle the lightly-damped resonant dynamics. A key aspect herein is the freedom in the multivariable rational model parametrizations. Several choices for such multivariable rational model parametrizations are proposed and investigated. For systems with many inputs and outputs the required number of model parameters can rapidly increase, adversely affecting the performance of the local modeling approach. Therefore, low-order model structures are investigated. The structure of these low-order parametrizations leads to an undesired directionality in the identification problem. To address this, an iterative local rational modeling algorithm is proposed. As a special case recently developed SISO algorithms are recovered. The proposed approach is successfully demonstrated on simulations and on an active vibration isolation system benchmark, confirming good performance of the method using significantly less parameters compared with alternative approaches.",
keywords = "Frequency response function, Local parametric modeling, Matrix fraction description, Non-parametric, System identification",
author = "R.J. Voorhoeve and {van der Maas}, A. and T.A.J. Oomen",
year = "2018",
month = "5",
day = "15",
doi = "10.1016/j.ymssp.2017.11.044",
language = "English",
volume = "105",
pages = "129--152",
journal = "Mechanical Systems and Signal Processing",
issn = "0888-3270",
publisher = "Academic Press Inc.",

}

Non-parametric identification of multivariable systems : a local rational modeling approach with application to a vibration isolation benchmark. / Voorhoeve, R.J.; van der Maas, A.; Oomen, T.A.J.

In: Mechanical Systems and Signal Processing, Vol. 105, 15.05.2018, p. 129-152.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Non-parametric identification of multivariable systems

T2 - a local rational modeling approach with application to a vibration isolation benchmark

AU - Voorhoeve, R.J.

AU - van der Maas, A.

AU - Oomen, T.A.J.

PY - 2018/5/15

Y1 - 2018/5/15

N2 - Frequency response function (FRF) identification is often used as a basis for control systems design and as a starting point for subsequent parametric system identification. The aim of this paper is to develop a multiple-input multiple-output (MIMO) local parametric modeling approach for FRF identification of lightly damped mechanical systems with improved speed and accuracy. The proposed method is based on local rational models, which can efficiently handle the lightly-damped resonant dynamics. A key aspect herein is the freedom in the multivariable rational model parametrizations. Several choices for such multivariable rational model parametrizations are proposed and investigated. For systems with many inputs and outputs the required number of model parameters can rapidly increase, adversely affecting the performance of the local modeling approach. Therefore, low-order model structures are investigated. The structure of these low-order parametrizations leads to an undesired directionality in the identification problem. To address this, an iterative local rational modeling algorithm is proposed. As a special case recently developed SISO algorithms are recovered. The proposed approach is successfully demonstrated on simulations and on an active vibration isolation system benchmark, confirming good performance of the method using significantly less parameters compared with alternative approaches.

AB - Frequency response function (FRF) identification is often used as a basis for control systems design and as a starting point for subsequent parametric system identification. The aim of this paper is to develop a multiple-input multiple-output (MIMO) local parametric modeling approach for FRF identification of lightly damped mechanical systems with improved speed and accuracy. The proposed method is based on local rational models, which can efficiently handle the lightly-damped resonant dynamics. A key aspect herein is the freedom in the multivariable rational model parametrizations. Several choices for such multivariable rational model parametrizations are proposed and investigated. For systems with many inputs and outputs the required number of model parameters can rapidly increase, adversely affecting the performance of the local modeling approach. Therefore, low-order model structures are investigated. The structure of these low-order parametrizations leads to an undesired directionality in the identification problem. To address this, an iterative local rational modeling algorithm is proposed. As a special case recently developed SISO algorithms are recovered. The proposed approach is successfully demonstrated on simulations and on an active vibration isolation system benchmark, confirming good performance of the method using significantly less parameters compared with alternative approaches.

KW - Frequency response function

KW - Local parametric modeling

KW - Matrix fraction description

KW - Non-parametric

KW - System identification

UR - http://www.scopus.com/inward/record.url?scp=85040453872&partnerID=8YFLogxK

U2 - 10.1016/j.ymssp.2017.11.044

DO - 10.1016/j.ymssp.2017.11.044

M3 - Article

AN - SCOPUS:85040453872

VL - 105

SP - 129

EP - 152

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

SN - 0888-3270

ER -