Distributed Model Predictive Control for Networked Plant-wide Systems With Neighborhood Cooperation

Ting Bai; Shaoyuan Li; Yi Zheng

doi:10.1109/JAS.2019.1911333

Volume 6 Issue 1

Jan. 2019

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(1): 108-117

Ting Bai, Shaoyuan Li and Yi Zheng, "Distributed Model Predictive Control for Networked Plant-wide Systems With Neighborhood Cooperation," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 108-117, Jan. 2019. doi: 10.1109/JAS.2019.1911333

Citation:

Ting Bai, Shaoyuan Li and Yi Zheng, "Distributed Model Predictive Control for Networked Plant-wide Systems With Neighborhood Cooperation," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 108-117, Jan. 2019. doi: 10.1109/JAS.2019.1911333

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Distributed Model Predictive Control for Networked Plant-wide Systems With Neighborhood Cooperation

doi: 10.1109/JAS.2019.1911333

Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China

Funds:

the National Nature Science Foundation of China 61590924

the National Nature Science Foundation of China 61673273

the National Nature Science Foundation of China 61833012

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Author Bio:
Ting Bai received the B.S. degree in automation from Northwestern Polytechnical University, Shaanxi, China, in 2013. She is a Ph.D. candidate in control science and engineering at Shanghai Jiao Tong University, Shanghai, China. Her research interests include distributed model predictive control, complex networked systems, with a recent focus on the input configuration design and distributed optimization control of reconfigurable architecture systems. (e-mail: number127578@sjtu.edu.cn)

Shaoyuan Li (SM'05) received the B.S. and M.S. degrees in automation from Hebei University of Technology, Tianjin, China, in 1987 and 1992, respectively, and the Ph.D. degree from the Department of Computer and System Science, Nankai University, Tianjin, in 1997. Since July 1997, he has been with the Department of Automation, Shanghai Jiao Tong University, Shanghai, China, where he is currently a Professor. His research interests include model predictive control, dynamic system optimization for large-scale networked systems. Professor Li is a Vice President of Asian Control Association (ACA) and a Senior Member of IEEE. (e-mail: syli@sjtu.edu.cn)

Yi Zheng (M'10) received the B.S. degree from Xi'an Jiao Tong University, Xian, China, in 2000, the M.E. degree from Shanghai University, Shanghai, China, in 2006, and the Ph.D. degree in automation from Shanghai Jiao Tong University, Shanghai, in 2010. He was with Shanghai Petrochemical Company Ltd., Shanghai, from 2000 to 2003, and the GE-Global Research, Shanghai, from 2010 to 2012. He was a Post-Doctoral Fellow with the University of Alberta, Edmonton, AB, Canada, from 2014 to 2015. Since 2012, he has been with Shanghai Jiao Tong University, where he is currently an Associate Professor. His research interests include distributed estimation, distributed model predictive control, optimization and their applications for industrial process, and smart grids. (e-mail: yizheng@sjtu.edu.cn)
Corresponding author: Shaoyuan Li, e-mail: syli@sjtu.edu.cn
Received Date: 2017-07-12
Revised Date: 2017-12-08
Accepted Date: 2018-05-04

Abstract

Abstract

For large-scale networked plant-wide systems composed by physically (or geographically) divided subsystems, only limited information is available for local controllers on account of region and communication restrictions. Concerning the optimal control problem of such subsystems, a neighbor-based distributed model predictive control (NDMPC) strategy is presented to improve the global system performance. In this scheme, the performance index of local subsystems and that of its neighbors are minimized together in the determination of the optimal control input, which makes the local control decision also beneficial to its neighboring subsystems and further contributes to improving the convergence and control performance of overall system. The stability of the closed-loop system is proved. Moreover, the parameter designing method for distributed synthesis is provided. Finally, the simulation results illustrate the main characteristics and effectiveness of the proposed control scheme.
- Distributed control,
- model predictive control (MPC),
- neighborhood cooperation,
- plant-wide systems

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References(27)

References

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Distributed Model Predictive Control for Networked Plant-wide Systems With Neighborhood Cooperation

doi: 10.1109/JAS.2019.1911333

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