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arXiv · 2112.08148

Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering

Ricarda-Samantha Götte·Julia Timmermann
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Abstract

In control design most control strategies are model-based and require accurate models to be applied successfully. Due to simplifications and the model-reality-gap physics-derived models frequently exhibit deviations from real-world-systems. Likewise, purely data-driven methods often do not generalise well enough and may violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired loss functions separately have shown promising results to conquer these drawbacks. In this contribution we extend existing methods towards the identification of non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN and a physics-inspired loss term (-L) to successfully identify the system's dynamics, while maintaining the consistency with physical laws. The proposed method is demonstrated on two real-world nonlinear systems and outperforms existing techniques regarding complexity and reliability.

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Ricarda-Samantha Götte, Julia Timmermann. 2021-12-15. Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering. https://doi.org/10.1109/airc56195.2022.9836982

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