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
收藏
页码：1492 / 1497
页数：6
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