论文标题
对控制的库普曼表示的深入学习
Deep Learning of Koopman Representation for Control
论文作者
论文摘要
我们为动态系统的最佳控制开发了一种数据驱动的，无模型的方法。提出的方法依赖于基于深层的神经网络（DNN）学习，以控制库普曼操作员。特别是，DNN用于数据驱动的识别非线性控制系统动力学线性提升中使用的基础函数。控制器合成纯粹是数据驱动的，不依赖于先验领域的知识。用于基于增强学习的控制设计的OpenAI健身环境用于控制环境中的Koopman操作员的数据生成和学习。该方法应用于OpenAI健身环境上的两个经典动力系统，以展示该功能。
We develop a data-driven, model-free approach for the optimal control of the dynamical system. The proposed approach relies on the Deep Neural Network (DNN) based learning of Koopman operator for the purpose of control. In particular, DNN is employed for the data-driven identification of basis function used in the linear lifting of nonlinear control system dynamics. The controller synthesis is purely data-driven and does not rely on a priori domain knowledge. The OpenAI Gym environment, employed for Reinforcement Learning-based control design, is used for data generation and learning of Koopman operator in control setting. The method is applied to two classic dynamical systems on OpenAI Gym environment to demonstrate the capability.
