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arXiv · 2310.20567

One-shot backpropagation for multi-step prediction in physics-based system identification -- EXTENDED VERSION

Cesare Donati·Martina Mammarella·Fabrizio Dabbene·Carlo Novara·Constantino Lagoa
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Abstract

The aim of this paper is to present a novel physics-based framework for the identification of dynamical systems, in which the physical and structural insights are reflected directly into a backpropagation-based learning algorithm. The main result is a method to compute in closed form the gradient of a multi-step loss function, while enforcing physical properties and constraints. The derived algorithm has been exploited to identify the unknown inertia matrix of a space debris, and the results show the reliability of the method in capturing the physical adherence of the estimated parameters.

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Cesare Donati, Martina Mammarella, Fabrizio Dabbene, Carlo Novara, Constantino Lagoa. 2023-11-21. One-shot backpropagation for multi-step prediction in physics-based system identification -- EXTENDED VERSION. https://doi.org/10.1016/j.ifacol.2024.08.540

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