Nonlinear model reduction based on stochastic obsevability

Kawamura, Taijiro; Yamakita, Masaki

doi:10.23919/acc45564.2020.9147688

2020 American Control Conference (ACC) 2020

DOI: 10.23919/acc45564.2020.9147688

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Nonlinear model reduction based on stochastic obsevability

Taijiro Kawamura

Masaki Yamakita

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2023

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Observability Gramian for Bayesian Inference in Nonlinear Systems With Its Industrial Application

Kunwoo

Umezu

Konno

et al. 2023

IEEE Control Syst. Lett.

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In this paper, we present a novel (empirical) observability Gramian for nonlinear stochastic systems in the light of Bayesian inference. First, we define our observability Gramian, which we refer to as the estimability Gramian, based on the relation to the so-called Bayesian Fisher Information Matrix for initial state estimation. Then, we study the fundamental properties of an empirical version of the estimability Gramian. The practical usefulness of the proposed framework is examined through its application to a parameter and initial state estimation in a natural gas engine cylinder.

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Observability Gramian for Bayesian Inference in Nonlinear Systems With Its Industrial Application

Kunwoo

Umezu

Konno

et al. 2023

IEEE Control Syst. Lett.

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show abstract

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Nonlinear model reduction based on stochastic obsevability

Cited by 1 publication

References 16 publications

Observability Gramian for Bayesian Inference in Nonlinear Systems With Its Industrial Application

Observability Gramian for Bayesian Inference in Nonlinear Systems With Its Industrial Application

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