可泛化的快速代理模型：基于物理信息神经网络的铰接式软体机器人模型预测控制
Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks
AI总结：
针对铰接式软体机器人控制中第一性原理模型速度慢、黑箱模型泛化差的问题，提出高数据效率的物理信息神经网络，实现了高速精准预测，支撑了47Hz频率下的非线性模型预测控制与精准位置跟踪。
摘要：
软体机器人可彻底变革对灵巧性和安全性要求极高的多个应用场景。运行这类系统时，实时估计与控制需要快速且精准的模型。但第一性原理（FP）模型的预测速度慢，而学习得到的黑箱模型泛化性较差。物理信息机器学习在这方面具备显著优势，但目前其应用局限于简单的、多为仿真的系统，未考虑训练后的变化。我们针对铰接式软体机器人（ASRs）提出聚焦数据效率的物理信息神经网络（PINNs），将昂贵的真实世界训练数据量降至最低——仅需单个系统域内的一个数据集。研究使用不同域内共两小时的数据，与两种金标准方法开展对比：与循环神经网络相比，该PINN具备高泛化性；在精度略有下降的情况下，该PINN的预测速度最高可达精准FP模型的467倍。这一特性使得气动铰接式软体机器人的非线性模型预测控制（MPC）成为可能，在6项动态实验中，运行频率达47Hz的MPC实现了精准的位置跟踪。
英文摘要：
Soft robots can revolutionize several applications with high demands on dexterity and safety. When operating these systems, real-time estimation and control require fast and accurate models. However, prediction with first-principles (FP) models is slow, and learned black-box models have poor generalizability. Physics-informed machine learning offers excellent advantages here, but it is currently limited to simple, often simulated systems without considering changes after training. We propose physics-informed neural networks (PINNs) for articulated soft robots (ASRs) with a focus on data efficiency. The amount of expensive real-world training data is reduced to a minimum -- one dataset in one system domain. Two hours of data in different domains are used for a comparison against two gold-standard approaches: In contrast to a recurrent neural network, the PINN provides a high generalizability. The prediction speed of an accurate FP model is exceeded with the PINN by up to a factor of 467 at slightly reduced accuracy. This enables nonlinear model predictive control (MPC) of a pneumatic ASR. Accurate position tracking with the MPC running at 47 Hz is achieved in six dynamic experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
