摘要：Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process. 漏 2023 The Authors

ISSN号：1672-5107

卷、期、页：卷21期1：597-608

发表日期：2024-02-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：一区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：Petroleum Science

通讯作者：苗兴园

第一作者：赵弘

论文类型：期刊论文

论文概要：苗兴园,赵弘，Dynamic plugging regulating strategy of pipeline robot based on reinforcement learning，Petroleum Science，2024，卷21期1：597-608

论文题目：Dynamic plugging regulating strategy of pipeline robot based on reinforcement learning