论文标题
通过深层计算对动态系统的模型控制
Model-Free Control of Dynamical Systems with Deep Reservoir Computing
论文作者
论文摘要
我们提出并演示了一种非线性控制方法，该方法可以应用于未知的复杂系统，其中控制器基于一种称为储层计算机的人工神经网络。与许多基于神经网络的控制技术相反，这些技术对系统的不确定性是强大的，但仍需要模型，我们的技术不需要对系统的先验知识，因此是无模型的。此外，我们的方法不需要初始的系统识别步骤，从而导致相对简单有效的学习过程。储层计算机非常适合控制问题，因为它们需要少量的培训数据集和较低的培训时间。通过迭代训练并将储层计算机添加到控制器中，很快就确定了精确有效的控制法。在数值和高速实验系统上均具有示例，我们证明了我们的方法能够控制高度复杂的动力学系统，这些系统将确定性的混乱与非平凡的目标轨迹显示。
We propose and demonstrate a nonlinear control method that can be applied to unknown, complex systems where the controller is based on a type of artificial neural network known as a reservoir computer. In contrast to many modern neural-network-based control techniques, which are robust to system uncertainties but require a model nonetheless, our technique requires no prior knowledge of the system and is thus model-free. Further, our approach does not require an initial system identification step, resulting in a relatively simple and efficient learning process. Reservoir computers are well-suited to the control problem because they require small training data sets and remarkably low training times. By iteratively training and adding layers of reservoir computers to the controller, a precise and efficient control law is identified quickly. With examples on both numerical and high-speed experimental systems, we demonstrate that our approach is capable of controlling highly complex dynamical systems that display deterministic chaos to nontrivial target trajectories.
