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

TL;DR

This paper introduces PGNN-L, a combined physics-guided neural network approach with physics-inspired loss, to improve system identification for non-autonomous control systems, balancing model accuracy and physical law adherence.

Contribution

It extends existing physics-guided neural networks to non-autonomous systems and proposes a new combined method PGNN-L that enhances identification accuracy and physical consistency.

Findings

Outperforms existing methods in complexity and reliability

Successfully identifies dynamics of real-world nonlinear systems

Maintains physical law consistency in system modeling

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.