Print Email Facebook Twitter Physics-Informed Neural Networks to Model and Control Robots Title Physics-Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation Author Liu, J. (TU Delft Learning & Autonomous Control) Borja, Pablo (Plymouth University) Della Santina, C. (TU Delft Learning & Autonomous Control; Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)) Date 2024 Abstract 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. Subject dissipationEuler–Lagrange equationsHamiltonian neural networksLagrangian neural networksmodel-based controlphysics-informed neural networksport-Hamiltonian systemsOA-Fund TU Delft To reference this document use: http://resolver.tudelft.nl/uuid:64b92602-8cc0-4ae6-ba59-0f18fc22122e DOI https://doi.org/10.1002/aisy.202300385 ISSN 2640-4567 Source Advanced Intelligent Systems Part of collection Institutional Repository Document type journal article Rights © 2024 J. Liu, Pablo Borja, C. Della Santina Files PDF Advanced_Intelligent_Syst ... al_and.pdf 5.26 MB Close viewer /islandora/object/uuid:64b92602-8cc0-4ae6-ba59-0f18fc22122e/datastream/OBJ/view