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.
CITATION STYLE
Liu, J., Borja, P., & Della Santina, C. (2024). Physics-Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation. Advanced Intelligent Systems, 6(5). https://doi.org/10.1002/aisy.202300385
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