Kernel-based continuous-time system identification: A parametric approximation

被引：3

作者：

Scandella, Matteo ^{[1
]}

Moreschini, Alessio ^{[1
]}

Parisini, Thomas ^{[1
,2
,3
]}

机构：

[1] Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England

[2] Univ Trieste, Dept Engn & Architecture, I-34127 Trieste, Italy

[3] Univ Cyprus, KIOS Res & Innovat Ctr Excellence, CY-1678 Nicosia, Cyprus

来源：

2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC | 2023年

基金：

英国工程与自然科学研究理事会;

关键词：

System Identification; Kernel-based learning; Loewner framework; Continuous-time system identification; LTI system identification; FREQUENCY-DOMAIN; MODELS; SPACES;

D O I：

10.1109/CDC49753.2023.10383442

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we discuss the non-parametric estimate problem using kernel-based LTI system identification techniques by constructing a Loewner-based interpolant of the estimated model. Through this framework, we have been able to retrieve a finite-dimensional approximation of the infinite-dimensional estimate obtained using the classical kernel-based methodologies. The employment of the Loewner framework constitutes an enhancement of recent results which propose to use a Pade approximant to obtain a rational transfer function from an irrational transfer function corresponding to the identified impulse response. The enhancement has been illustrated for the identification of the Rao-Garnier benchmark.

引用

页码：1492 / 1497

页数：6

共 43 条

[1]

Antoulas A.C., 2011, Model Reduction and Approximation, P335, DOI DOI 10.1137/1.9781611974829.CH8

[2] GENERALIZED SYSTEM IDENTIFICATION WITH STABLE SPLINE KERNELS [J].

Aravkin, Aleksandr Y. ;

Burke, James, V ;

Pillonetto, Gianluigi .

SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2018, 40 (05) :B1419-B1443

[3]

Argyriou A, 2014, PR MACH LEARN RES, V32, P748

[4] THEORY OF REPRODUCING KERNELS [J].

ARONSZAJN, N .

TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY, 1950, 68 (MAY) :337-404

[5]

Baker GAJr., 1996, PADE APPROXIMANTS, V2nd end, DOI DOI 10.1017/CBO9780511530074

[6]

Bittanti S., 2019, Model Identification and Data Analysis

[7] Kernel-based identification of non-causal systems with application to inverse model control [J].

Blanken, Lennart ;

Oomen, Tom .

AUTOMATICA, 2020, 114

[8] The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements [J].

Cavanaugh, Joseph E. ;

Neath, Andrew A. .

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2019, 11 (03)

[9] EM-based identification of continuous-time ARMA Models from irregularly sampled data [J].

Chen, Fengwei ;

Aguero, Juan C. ;

Gilson, Marion ;

Garnier, Hugues ;

Liu, Tao .

AUTOMATICA, 2017, 77 :293-301

[10] Robust identification of continuous-time models with arbitrary time-delay from irregularly sampled data [J].

Chen, Fengwei ;

Garnier, Hugues ;

Gilson, Marion .

JOURNAL OF PROCESS CONTROL, 2015, 25 :19-27

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