论文标题
学习可稳定的深度动态模型
Learning Stabilizable Deep Dynamics Models
论文作者
论文摘要
当使用神经网络来建模动力学时，通常不能保证诸如动力学的稳定性之类的属性。相比之下，有一种学习自主系统动力学的方法，可以通过神经网络确保全球指数稳定性。在本文中，我们提出了一种学习输入式控制系统动态的新方法。一个重要的特征是，也获得了学习模型的稳定控制器和控制lyapunov功能。此外，提出的方法还可以应用于解决汉密尔顿 - 雅各比的不平等现象。通过数值示例检查了所提出方法的有用性。
When neural networks are used to model dynamics, properties such as stability of the dynamics are generally not guaranteed. In contrast, there is a recent method for learning the dynamics of autonomous systems that guarantees global exponential stability using neural networks. In this paper, we propose a new method for learning the dynamics of input-affine control systems. An important feature is that a stabilizing controller and control Lyapunov function of the learned model are obtained as well. Moreover, the proposed method can also be applied to solving Hamilton-Jacobi inequalities. The usefulness of the proposed method is examined through numerical examples.
