This work concerns the application of physics-informed neural networks to the modeling and control of complex robotic systems. Achieving this goal required extending Physics Informed Neural Networks to handle non-conservative effects. We propose to combine these learned models with model-based controllers originally developed with first-principle models in mind. By combining standard and new techniques, we can achieve precise control performance while proving theoretical stability bounds. These validations include real-world experiments of motion prediction with a soft robot and of trajectory tracking with a Franka Emika manipulator.

翻译：本工作研究了物理信息神经网络在复杂机器人系统建模与控制中的应用。为实现该目标，需将物理信息神经网络扩展以适应非保守效应。我们提出将此类学习模型与最初基于第一性原理开发的模型控制器相结合。通过融合标准技术与新技术，可在保证理论稳定性边界的同时实现精确控制性能。相关验证涵盖软体机器人的运动预测实验以及Franka Emika机械臂的轨迹跟踪实验。