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Kernel-based identification of asymptotically stable continuous-time linear dynamical systems
International Journal of Control ( IF 1.6 ) Pub Date : 2021-01-11 , DOI: 10.1080/00207179.2020.1868580
Matteo Scandella 1 , Mirko Mazzoleni 1 , Simone Formentin 2 , Fabio Previdi 1
Affiliation  

ABSTRACT

In many engineering applications, continuous-time models are preferred to discrete-time ones, in that they provide good physical insight and can be derived also from non-uniformly sampled data. However, for such models, model selection is a hard task if no prior physical knowledge is given. In this paper, we propose a non-parametric approach to infer a continuous-time linear model from data, by automatically selecting a proper structure of the transfer function and guaranteeing to preserve the system stability properties. By means of benchmark simulation examples, the proposed approach is shown to outperform state-of-the-art continuous-time methods, also in the critical case when short sequences of canonical input signals, like impulses or steps, are used for model learning.



中文翻译:

基于核的渐近稳定连续时间线性动力系统识别

摘要

在许多工程应用中,连续时间模型优于离散时间模型,因为它们提供了良好的物理洞察力,并且还可以从非均匀采样的数据中导出。然而,对于这样的模型,如果没有先验物理知识,模型选择是一项艰巨的任务。在本文中,我们提出了一种非参数方法,通过自动选择传递函数的适当结构并保证保持系统稳定性特性,从数据中推断出连续时间线性模型。通过基准仿真示例,所提出的方法被证明优于最先进的连续时间方法,在关键情况下,当标准输入信号的短序列(如脉冲或步骤)用于模型学习时也是如此。

更新日期:2021-01-11
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