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

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.

Keywords

• dissipation
• Euler–Lagrange equations
• Hamiltonian neural networks
• Lagrangian neural networks
• model‐based control
• physics‐informed neural networks