Petroleum Science >2024,??Issue1:??- DOI: https://doi.org/10.1016/j.petsci.2023.08.016
Dynamic plugging regulating strategy of pipeline robot based on reinforcement learning Open?Access
文章信息
作者：Xing-Yuan Miao, Hong Zhao
作者單位：
投稿時間：
引用方式：Xing-Yuan Miao, Hong Zhao, Dynamic plugging regulating strategy of pipeline robot based on reinforcement learning, Petroleum Science, Volume 21, Issue 1, 2024, Pages 597-608, https://doi.org/10.1016/j.petsci.2023.08.016.
文章摘要
Abstract: 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.
關鍵詞
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Keywords: Pipeline isolation plugging robot; Plugging-induced vibration; Dynamic regulating strategy; Extreme learning machine; Improved sparrow search algorithm; Modified Q-learning algorithm