Kernel-based identification of asymptotically stable continuous-time linear dynamical systems

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.

Automated summary

In this paper, a non-parametric approach to infer a continuous-time linear model from data is proposed, which automatically selects a proper structure of the transfer function and guarantees system stability. Through benchmark simulation examples, the proposed approach is shown to outperform state-of-the-art continuous-time methods, even in critical cases where short sequences of canonical input signals are used for model learning.