Liu, J., Borja, P. and Della Santina, C. (2024), Physics-Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation. Adv. Intell. Syst., 6: 2300385. doi: 10.1002/aisy.202300385

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.