Continuous-time parametric models of dynamical systems are usually preferred given their physical interpretation. When there is a lack of prior physical knowledge, the user is faced with the model selection issue. In this paper, we propose a non-parametric approach to estimate a continuous-time stable linear model from data, while automatically selecting a proper structure of the transfer function and guaranteeing to preserve the system stability properties. Results show how the proposed approach outperforms the state of the art.

(2020). Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach . Retrieved from http://hdl.handle.net/10446/171532

Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach

Mazzoleni, Mirko;Scandella, Matteo;Previdi, Fabio
2020-01-01

Abstract

Continuous-time parametric models of dynamical systems are usually preferred given their physical interpretation. When there is a lack of prior physical knowledge, the user is faced with the model selection issue. In this paper, we propose a non-parametric approach to estimate a continuous-time stable linear model from data, while automatically selecting a proper structure of the transfer function and guaranteeing to preserve the system stability properties. Results show how the proposed approach outperforms the state of the art.
2020
Mazzoleni, Mirko; Scandella, Matteo; Formentin, Simone; Previdi, Fabio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/171532
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