Adaptive Regulator for Natural Gas Transportation based on DDPG

In this paper, we propose an adaptive regulator for natural gas transportation based on Deep Deterministic Policy Gradient (DDPG). Currently, most natural gas pipeline pressure and flow control rely on traditional control methods. However, the transportation issues of natural gas pipelines are subject to frequent environmental changes. For instance, variations in factors such as temperature and density of natural gas can necessitate cumbersome readjustment and design of traditional controllers. While the DDPG controller can automatically learn and optimize the control strategy during the training process by setting appropriate performance indicators. This makes the DDPG controller more convenient and flexible, suitable for a wide range of control tasks. The paper constructs a model of the natural gas pipeline environment, designs an appropriate control scheme, and simultaneously develops a DDPG controller, along with relevant performance indicators. Through the established natural gas pipeline model, simulation experiments verify the effectiveness of the DDPG control algorithm.