Physics‐Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation

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This work concerns the application of physics‐informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics‐informed neural networks to handle nonconservative effects. These learned models are proposed to combine with model‐based controllers originally developed with first‐principle models in mind. By combining standard and new techniques, precise control performance can be achieved while proving theoretical stability bounds. These validations include real‐world experiments of motion prediction with a soft robot and trajectory tracking with a Franka Emika Panda manipulator.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Physics‐Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation ; day:23 ; month:02 ; year:2024 ; extent:17
Advanced intelligent systems ; (23.02.2024) (gesamt 17)

Creator: Liu, Jingyue
Borja, Pablo
Della Santina, Cosimo

DOI: 10.1002/aisy.202300385

URN: urn:nbn:de:101:1-2024022414040623457423

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:59 AM CEST

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Liu, Jingyue
Borja, Pablo
Della Santina, Cosimo

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Physics‐Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation

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