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FRF estimation using multiple kernel-based regularisation
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DOI：10.1016/j.automatica.2021.110056.png)
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Abstract
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Nonparametric kernel-based modelling of dynamical systems offers important advantages over other nonparametric techniques; the estimate is a continuous function, the model complexity is continuously tuneable, and stability, causality and smoothness are imposed on the impulse response estimate. However, for lightly damped systems, most of the existing kernel-based approaches for estimating the impulse-or frequency response function fail because classical kernels are not appropriate for describing lowly damped resonances. Smoothness is imposed on the entire frequency axis with the diagonal correlated or stable spline kernel, with as a result that resonances cannot be captured well. By introducing the superposition of different kernels, carrying prior knowledge about the resonant poles of the system, we make the kernel-based modelling of lightly-damped systems possible with high-accuracy. We use a frequency domain local rational modelling technique as preprocessing step to determine the most dominant poles, and include these as prior knowledge in the kernels. The performance of the new kernel is demonstrated on a highly resonating simulated system and compared to the state of the art nonparametric frequency domain approaches. (C) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Data-driven modelling
Kernel-based modelling
Local rational modelling
Gaussian process regression
Machine learning
Lightly damped systems
Journal
IF:
5.9
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1.2W
Citations:
5.2W
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AUTOMATICA
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AUTOMATICAIF5.9
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