论文标题
深度Koopman控制：软机器人动力学的光谱分析
Deep Koopman with Control: Spectral Analysis of Soft Robot Dynamics
论文作者
论文摘要
软机器人对建模和控制的挑战是固有的非线性（例如弹性和变形），通常需要复杂的基于物理的显式分析建模（例如先验几何定义）。尽管机器学习可用于在数据驱动的方法中学习非线性控制模型，但这些模型通常缺乏直观的内部物理解释和表示，从而限制了动态分析。为了解决这个问题，本文使用Koopman操作员理论和深层神经网络介绍了一种方法，以提供非线性控制系统的全球线性描述。具体而言，通过全局线性化的动力学，使用光谱分解来分析库普曼操作员，以表征重要的基于物理学的解释，例如功能生长和振荡。本文的实验证明了这种控制非线性软机器人技术的方法，并显示模型输出在光谱分析的背景下是可以解释的。
Soft robots are challenging to model and control as inherent non-linearities (e.g., elasticity and deformation), often requires complex explicit physics-based analytical modeling (e.g., a priori geometric definitions). While machine learning can be used to learn non-linear control models in a data-driven approach, these models often lack an intuitive internal physical interpretation and representation, limiting dynamical analysis. To address this, this paper presents an approach using Koopman operator theory and deep neural networks to provide a global linear description of the non-linear control systems. Specifically, by globally linearising dynamics, the Koopman operator is analyzed using spectral decomposition to characterises important physics-based interpretations, such as functional growths and oscillations. Experiments in this paper demonstrate this approach for controlling non-linear soft robotics, and shows model outputs are interpretable in the context of spectral analysis.
