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

Autor:	Jingyue Liu, Pablo Borja, Cosimo Della Santina
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	dissipation Euler–Lagrange equations Hamiltonian neural networks Lagrangian neural networks model‐based control physics‐informed neural networks Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225
Zdroj:	Advanced Intelligent Systems, Vol 6, Iss 5, Pp n/a-n/a (2024)
Druh dokumentu:	article
ISSN:	2640-4567
DOI:	10.1002/aisy.202300385
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/7aa8e438c1b34390b1fecb260cad9179 Zobrazit plný text záznamu Plný text View record in DOAJ