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

He, Jing; Su, Shijie; Wang, Hairong; Chen, Fan; Yin, Baoji

doi:10.3390/machines11060593

Cited by 9 publications

(2 citation statements)

References 31 publications

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“…Where L q is the output flow of the proportional directional valve, q K is the flow gain coefficient, c K is the flow-pressure coefficient of the proportional directional valve, The relationship between the spool displacement and the control signal in a proportional directional valve can be characterized as follows [16]:…”

Section: B Hydraulic Servo Systems Modelmentioning

confidence: 99%

Control Strategy of Hydraulic Servo Control Systems Based on the Integration of Soft Actor-Critic and Adaptive Robust Control

He,

Zhou,

et al. 2024

IEEE Access

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High-flow hydraulic servo systems are extensively employed in contemporary industrial applications due to their considerable flow capacity and cost-effectiveness. Nonetheless, hydraulic servo systems frequently encounter unpredictable internal and external disturbances, and high-flow proportional directional valves always have unsatisfactory hydraulic characteristics, compromising the precision and robustness of high-flow hydraulic servo systems. This study proposes a novel control strategy integrating the Soft Actor-Critic (SAC) reinforcement learning algorithm with Adaptive Robust Control (ARC) to enhance system performance. This approach features a two-tiered controller: the upper controller utilizes the SAC algorithm to learn and adapt to the dynamics of the hydraulic servo system, iteratively refining the lower controller's hyperparameters. Meanwhile, grounded in the ARC strategy, the lower controller executes realtime control of the hydraulic servo system. The simulation and experimental results demonstrate that the proposed control strategy can effectively adjust the control hyperparameters according to the learned system dynamic and tracking errors. Consequently, this approach enhances control precision amidst varying external and internal disturbances. Moreover, this control strategy is anticipated to realize high-flow, high-precision, and high-robust hydraulic servo systems, which can be used in various fields such as marine and offshore engineering.

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Section: B Hydraulic Servo Systems Modelmentioning

confidence: 99%

Control Strategy of Hydraulic Servo Control Systems Based on the Integration of Soft Actor-Critic and Adaptive Robust Control

He,

Zhou,

et al. 2024

IEEE Access

Self Cite

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“…A fuzzy PID controller [33], as shown in Figure 8, is used to accurately control the pressure in clutch brake cavity 5.…”

Section: Fuzzy Controller Designmentioning

confidence: 99%

Research on Flexible Braking Control of a Crawler Crane during the Free-Fall Hook Process

Gao,

Song,

Yang

et al. 2024

Processes

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Due to the large inertia and strong impact accompanying the free-falling hook process of crawler cranes, it is difficult to meet the demand for flexible and smooth braking control under different weight load conditions. Therefore, this paper takes the free-fall hook system as the research object and combines system operation characteristics and control theory to carry out research on flexible braking control of the free-fall hook system. Firstly, a joint simulation platform of MATLAB (version 2018b) and AMESim (version 2019.1) software is built to theoretically analyze the key components of the free-fall hook system (proportional pressure-reducing valve, winch reducer, and wet clutch). Secondly, a mathematical model of the braking process is established, and the pressure control demand is clarified to analyze the reasons for the existence of dead zones and hysteresis loops in the system. Meanwhile, it is found that the dead zones and hysteresis loops existing in the pressure output of the pressure-reducing valve are the main factors of flexibility with load braking. Then, in this paper, a closed-loop control strategy is formulated based on the automatic adaptation of the braking gear in combination with the fuzzy PID pressure. Finally, the effectiveness of the control strategy proposed in this paper is verified with simulation and experimental testing using the pressure hysteresis loop of the free-fall hook process and the load-braking acceleration as the judging criteria. The results show that the system pressure hysteresis loop is reduced by 50%–60% and the maximum braking acceleration is reduced by 24%–30% under the conditions of 6.44 tonnes and 10.44 tonnes, which improves the accuracy of pressure control and achieves flexible and smooth braking with loads for different tonnages of free-fall hooks.

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Optimizing olefin purification: An artificial intelligence-based process-conscious PI controller tuning for double dividing wall column distillation

Syauqi,

Kim,

Lim

2024

Chemical Engineering Journal

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

Cited by 9 publications

References 31 publications

Control Strategy of Hydraulic Servo Control Systems Based on the Integration of Soft Actor-Critic and Adaptive Robust Control

Control Strategy of Hydraulic Servo Control Systems Based on the Integration of Soft Actor-Critic and Adaptive Robust Control

Research on Flexible Braking Control of a Crawler Crane during the Free-Fall Hook Process

Optimizing olefin purification: An artificial intelligence-based process-conscious PI controller tuning for double dividing wall column distillation

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