Energy-Saving for a Velocity Control System of a Pipe Isolation Tool Based on a Reinforcement Learning Method

Wu, Tingting; Hong, Zhonghua; Gao, Boxuan; Meng, Fanbo

doi:10.1007/s40684-021-00309-8

Cited by 15 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wu et al applied the Q-learning algorithm to a real-time control energy-saving system in mine operation. The control algorithm learns the energy distribution of hydraulic pumps and accumulators and then adjusts the opening of pumps and accumulator valves to achieve energy savings [19]. Egli et al applied an R.L.…”

Section: Introductionmentioning

confidence: 99%

Online PID Tuning Strategy for Hydraulic Servo Control Systems via SAC-Based Deep Reinforcement Learning

Wang

et al. 2023

Machines

View full text Add to dashboard Cite

Proportional–integral–derivative (PID) control is the most common control technique used in hydraulic servo control systems. However, the nonlinearity and uncertainty of the hydraulic system make it challenging for PID control to achieve high-precision control. This paper proposes a novel control strategy that combines the soft actor-critic (SAC) reinforcement learning algorithm with the PID method to address this issue. The proposed control strategy consists of an upper-level controller based on the SAC algorithm and a lower-level controller based on the PID control method. The upper-level controller continuously tunes the control parameters of the lower-level controller based on the tracking error and system status. The lower-level controller performs real-time control for the hydraulic servo system with a control frequency 10 times higher than the upper controllers. Simulation experiments demonstrate that the proposed SAC-PID control strategy can effectively address disturbances and achieve high precision control for hydraulic servo control systems in uncertain working conditions compared with PID and fuzzy PID control methods. Therefore, the proposed control strategy offers a promising approach to improving the tracking performance of hydraulic servo systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Online PID Tuning Strategy for Hydraulic Servo Control Systems via SAC-Based Deep Reinforcement Learning

Wang

et al. 2023

Machines

View full text Add to dashboard Cite

show abstract

“…Wu designed a velocity-tracking control system for an IPT [17], in which energy recovery could be carried out during the plugging process. The reinforcement learning method was adopted to improve energy efficiency [18].…”

Section: Introductionmentioning

confidence: 99%

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

Miao

Hong

2022

J. Pipeline Syst. Eng. Pract.

Self Cite

View full text Add to dashboard Cite

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 Bi-LSTM (bidirectional long short-term memory) 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 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.

show abstract

Deep Reinforcement Learning for Continuous Control of Material Thickness

Dippel,

Lisitsa,

Peng

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Energy-Saving for a Velocity Control System of a Pipe Isolation Tool Based on a Reinforcement Learning Method

Cited by 15 publications

References 18 publications

Online PID Tuning Strategy for Hydraulic Servo Control Systems via SAC-Based Deep Reinforcement Learning

Online PID Tuning Strategy for Hydraulic Servo Control Systems via SAC-Based Deep Reinforcement Learning

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

Deep Reinforcement Learning for Continuous Control of Material Thickness

Contact Info

Product

Resources

About