Vibration Reduction Control of In-Pipe Intelligent Isolation Plugging Tool Based on Deep Reinforcement Learning

Compared with traditional plugging methods, the in-pipe intelligent isolation plugging tool (IPT) is advantageous in safety and work efficiency. However, during the plugging process, the flow field around the IPT changes drastically, resulting in vortex-induced vibration and potential failure of the plugging operation. In this study, three foldable spoilers were designed at the tail of the IPT to optimize the flow field. The vibration of the IPT can be alleviated by adjusting the angles of the spoilers. A vibration reduction control system of the IPT was designed based on deep reinforcement learning. First, we conducted an experiment for vibration reduction system. Second, a nonlinear model of the pressure difference based on experimental data was established. Then, a multi-agent self-learning system based on the deep Q-network (DQN) was designed, and the optimal actions were selected in each agent to adjust the spoiler angles during the plugging process. Finally, a controller based on fuzzy reinforcement learning was proposed to flip the spoilers to the optimized angles. The results show that the vibration reduction control system of the IPT reduced the pressure difference by an average of 28.32%, which indicates the stability of the plugging process and a successful reduction of the IPT vibration.

Automated summary

Compared with traditional plugging methods, the in-pipe intelligent isolation plugging tool (IPT) has advantages in safety and work efficiency. This study successfully optimized the flow field and vibration of IPT through the design of foldable spoilers and a vibration reduction control system, achieving stability in the plugging process and successfully reducing the vibration of IPT.