Data-Driven Time-Delay Optimal Control Method for Roller Kiln Temperature Field

Jiayao Chen; Weihua Gui; Ning Chen; Biao Luo; Binyan Li; Zeng Luo; Chunhua Yang

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > In Press, Accepted Manuscript

J. Chen, W. Gui, N. Chen, B. Luo, B. Li, Z. Luo, and C. Yang, “Data-driven time-delay optimal control method for roller kiln temperature field,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 9, pp. 1–12, Sept. 2025.

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J. Chen, W. Gui, N. Chen, B. Luo, B. Li, Z. Luo, and C. Yang, “Data-driven time-delay optimal control method for roller kiln temperature field,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 9, pp. 1–12, Sept. 2025.

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Data-Driven Time-Delay Optimal Control Method for Roller Kiln Temperature Field

Funds: This work was supported in part by the Key Program of National Natural Science Foundation of China (62033014), the Application Projects of Integrated Standardization and New Paradigm for Intelligent Manufacturing from the Ministry of Industry and Information Technology of China in 2016, and the Fundamental Research Funds for the Central Universities of Central South University (2021zzts0700)

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Author Bio:
Jiayao Chen received the B.S. degree from Northeast University in 2019. She is currently pursuing the Ph.D. degree with the School of Automation, Institute of Control Engineering, Central South University. Her research interests include modeling and control of temperature field

Weihua Gui (Member, IEEE) received the M.Eng. degree in automatic control engineering from Central South University in 1981.From 1986 to 1988, he was a visiting scholar with the University of Duisburg-Essen, Germany. Since 1991, he has been a Professor with the School of Automation, Central South University. Since 2013, he has been an Academician with the Chinese Academy of Engineering. His research interests include modeling and optimal control of complex industrial processes, distributed robust control, and fault diagnosis

Ning Chen (Member, IEEE) received the B.S. and M.S. degrees in industrial electrical automation and the Ph.D. degree in control theory and engineering from the Central South University in 1992, 1995, and 2002, respectively.She is a Professor with the School of Automation, Central South University. Her research interests include modeling and optimization control of complex industrial process, intelligent manufacturing of battery materials, and adaptive dynamic programming

Biao Luo (Senior Member, IEEE) received the Ph.D. degree in control science and engineering from Beihang University in 2014.He was an Associate Professor and an Assistant Professor with the Institute of Automation, Chinese Academy of Sciences from 2014 to 2018. He is currently a Professor with the School of Automation, Central South University and the Peng Cheng Laboratory. His research interests include distributed parameter systems, intelligent control, reinforcement learning, deep learning, and computational intelligence.Prof. Luo was a recipient of the Chinese Association of Automation Outstanding Ph.D. Dissertation Award in 2015. He serves as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, the IEEE Transactions on Emerging Topics in Computational Intelligence, the Artificial Intelligence Review, Neurocomputing, and the Journal of Industrial and Management Optimization

Binyan Li received the B.S. and M.S. degrees from the School of Information Science, Xiangtan University in 2014 and 2017, respectively, and the Ph.D. degree from the School of Automation, Institute of Control Engineering, Central South University in 2022. She is currently a Lecturer with the School of Electrical and Information Engineering, Changsha University of Science and Technology. Her research interests include modeling and optimal control of distributed parameter systems, such as temperature field in roller kiln, event-triggered optimal control, and adaptive dynamic programming

Zeng Luo received the B.S. degree from the School of Energy and Electromechanical Engineering, Hunan University of Humanities, Science and Technology in 2022. He is currently pursuing the master degree with the School of Automation, Institute of Control Engineering, Central South University. His research interests include optimal control of large-scale interconnected systems, time delay, decentralized control, distributed control, and adaptive dynamic programming

Chunhua Yang (Fellow, IEEE) received the M.Eng. degree in automatic control engineering and the Ph.D. degree in control science and engineering from Central South University in 1988 and 2002, respectively. From 1999 to 2001, she was with the Electrical Engineering Department, Katholieke Universiteit Leuven, Belgium. She is a Full Professor with the School of Automation, Central South University. Her research interests include modeling and optimal control of complex industrial process, intelligent control systems, and online detection technology
Corresponding author: Ning Chen, e-mail: ningchen@csu.edu.cn
Received Date: 2024-08-05
Revised Date: 2024-12-12
Accepted Date: 2025-02-03

Available Online: 2025-08-05

Abstract

Abstract

In the industrial roller kiln, the time-delay characteristic in heat transfer causes the temperature field to be affected by both the current and historical temperature states. It presents a poor control performance and brings a significant challenge to the process precise control. Considering high complexity of precise modeling, a data-driven time-delay optimal control method for temperature field of roller kiln is proposed based on a large amount of process data. First, the control challenges and problem description brought by time-delay are demonstrated, where the cost function for the time-delay partial differential equation system is constructed. To obtain the optimal control law, the policy iteration in adaptive dynamic programming is adopted to design the time-delay temperature field controller, and neural network is used for the critic network in policy iteration to approximate the optimal time-delay cost function. The closed-loop system stability is proved by designing the Lyapunov function which contains the time-delay information. Finally, through establishing the time-delay temperature field model for roller kiln, the effectiveness and convergence of the proposed method is verified and proved.
- Distributed parameter system,
- optimal control,
- policy iteration,
- roller kiln,
- time-delay temperature field

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References(42)

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Data-Driven Time-Delay Optimal Control Method for Roller Kiln Temperature Field

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