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

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...