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

Liu, J.; Borja, Pablo; Della Santina, C.

doi:10.1002/aisy.202300385

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

dissipation
Euler–Lagrange equations
Hamiltonian neural networks
Lagrangian neural networks
model-based control
physics-informed neural networks
port-Hamiltonian systems

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