Physics-Informed Neural Networks to Model and Control Robots
A Theoretical and Experimental Investigation
Jingyue Liu (TU Delft - Learning & Autonomous Control)
Pablo Borja (Plymouth University)
C. Della Santina (Deutsches Zentrum für Luft- und Raumfahrt (DLR), TU Delft - Learning & Autonomous Control)
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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.
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