具有Backstepping预训练DeepONet的Soft Actor-Critic用于PDE控制
Soft Actor-Critic with Backstepping-Pretrained DeepONet for control of PDEs
AI总结：
本文提出一种结合Backstepping预训练DeepONet与SAC的强化学习方法，用于稳定PDE系统，优于传统SAC和Backstepping控制器。
摘要：
本文开发了一种基于强化学习的控制器，用于稳定偏微分方程（PDE）系统。在Soft Actor-Critic（SAC）框架中，我们嵌入了一个著名的神经算子（NO），即DeepONet，该算子通过Backstepping控制器进行预训练。预训练的DeepONet捕捉了Backstepping控制器的本质特征，并作为特征提取器，取代了原始Actor和Critic网络中的卷积神经网络（CNN）层，并直接连接到SAC架构的全连接层。我们将这种新颖的Backstepping和强化学习集成方法应用于稳定一个不稳定的一阶超几何PDE和一个不稳定的反应扩散PDE。仿真结果表明，所提出的方法在两个系统上均优于标准SAC、带有未训练DeepONet的SAC以及Backstepping控制器。
英文摘要：
This paper develops a reinforcement learning-based controller for the stabilization of partial differential equation (PDE) systems. Within the soft actor-critic (SAC) framework, we embed a DeepONet, a well-known neural operator (NO), which is pretrained using the backstepping controller. The pretrained DeepONet captures the essential features of the backstepping controller and serves as a feature extractor, replacing the convolutional neural networks (CNNs) layers in the original actor and critic networks, and directly connects to the fully connected layers of the SAC architecture. We apply this novel backstepping and reinforcement learning integrated method to stabilize an unstable ffrst-order hyperbolic PDE and an unstable reactiondiffusion PDE. Simulation results demonstrate that the proposed method outperforms the standard SAC, SAC with an untrained DeepONet, and the backstepping controller on both systems.
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。
