Retzler, András and Tóth, Roland and Schoukens, M and Beintema, G I and Weigand, J and Noël, J-P and Kollár, Zsolt and Swevers, J (2024) Learning-based augmentation of physics-based models: an industrial robot use case. Data-Centric Engineering, 5. ISSN 2632-6736 10.1017/dce.2024.8

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Abstract

In a Model Predictive Control (MPC) setting, the precise simulation of the behavior of the system over a finite time window is essential. This application-oriented benchmark study focuses on a robot arm that exhibits various nonlinear behaviors. For this arm, we have a physics-based model with approximate parameter values and an open benchmark dataset for system identification. However, the long-term simulation of this model quickly diverges from the actual arm’s measurements, indicating its inaccuracy. We compare the accuracy of black-box and purely physics-based approaches with several physics-informed approaches. These involve different combinations of a neural network’s output with information from the physics-based model or feeding the physics-based model’s information into the neural network. One of the physics-informed model structures can improve accuracy over a fully black-box model.

Item Type:	Article
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Systems and Control Lab
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	04 Sep 2024 07:43
Last Modified:	04 Sep 2024 07:43
URI:	https://eprints.sztaki.hu/id/eprint/10781

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