App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 8

Industrial process control method based on second-order value gradient model reinforcement learning

Zhang Bo1,2,3,4
Pan Fucheng1,2,3
Zhou Xiaofeng1,2,3
Li Shuai1,2,3
1. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3. Institutes for Robotics Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4. University of Chinese Academy of Sciences, Beijing 100049, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0580

Abstract

To achieve stable and accurate control of complex industrial processes with high latency, nonlinearity, and strong coupling, this paper proposed a control method based on second-order value function gradient model reinforcement learning. Firstly, during the model training process, the method incorporated second-order gradient information of the state-value function, enabling more accurate function approximation and higher robustness, resulting in improved learning iteration efficiency. Secondly, by adopting a new state sampling strategy, this method facilitated more effective utilization of the model for policy learning. Lastly, experiments conducted in the OpenAI Gym public environments and simulated environments of two industrial scenarios demonstrated that compared to traditional maximum likelihood estimation models, the second-order value gradient model significantly reduced the prediction error of the environment model. In addition, the reinforcement learning method based on the second-order value gradient model exhibited higher learning efficiency than existing model-based policy optimization methods, showcasing better control performance and mitigating oscillation phenomena during the control process. In conclusion, the proposed method effectively enhances training efficiency while improving the stability and accuracy of industrial process control.

Foundation Support

中国科学院沈阳自动化研究所基础研究计划项目(2022000346)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0580
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 8

Publish History

[2024-02-05] Accepted Paper

Cite This Article

张博, 潘福成, 周晓锋, 等. 基于二阶价值梯度模型强化学习的工业过程控制方法 [J]. 计算机应用研究, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0580. (Zhang Bo, Pan Fucheng, Zhou Xiaofeng, et al. Industrial process control method based on second-order value gradient model reinforcement learning [J]. Application Research of Computers, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0580. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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