Regulating Control of In-Pipe Intelligent Isolation Plugging Tool Based on Adaptive Dynamic Programming

Abstract

In-pipe intelligent isolation plugging tools (IPTs) are crucial in pipeline maintenance. During the plugging process, the flow field around the IPT changes drastically, resulting in vibration and instability of the plugging process. Therefore, three foldable spoilers were designed at the tail of the IPT to reduce the vibration of the IPT. First, a disturbing flow experiment of IPT with spoilers was designed. A mathematical model of the pneumatic spoiler control system was established to regulate the spoiler angles. Second, based on the experimental data, a bidirectional long short-term memory (Bi-LSTM) neural network predictor between the plugging states, the spoiler angles, and the pressure gradient was established. Then, an adaptive dynamic programming controller was designed to select the optimal control action for each plugging state, thereby reducing the pressure gradient. Finally, Python and MATLAB/Simulink were used for simulation. The results showed that prediction errors were controlled within 9%, and the controller could reduce the pressure gradient during the plugging process by an average of 25.94%, which alleviated the vibration of the IPT and achieved a smooth plugging operation.