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Dynamic plugging regulating strategy of pipeline robot based on reinforcement learning

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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 math-ematical 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.

Pipeline isolation plugging robotPlugging-induced vibrationDynamic regulating strategyExtreme learning machineImproved sparrow search algorithmModified Q-learning algorithm

Xing-Yuan Miao、Hong Zhao

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College of Mechanical and Transportation Engineering,China University of Petroleum,Beijing,102249,China

国家自然科学基金Science Foundation of China University of Petroleum,Beijing

515755282462022QEDX011

2024

10.1016/j.petsci.2023.08.016

石油科学(英文版)
中国石油大学（北京）

石油科学(英文版)

EI
影响因子：0.88
ISSN：1672-5107
年,卷(期)：2024.21(1)
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