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

M. Mazzoleni, M. Scandella, S. Formentin, F. Previdi

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.

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