Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor

doi:10.1016/j.cjche.2016.08.018

›› 2017, Vol. 25 ›› Issue (9): 1230-1237.DOI: 10.1016/j.cjche.2016.08.018

• Process Systems Engineering and Process Safety • 上一篇下一篇

Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor

Pengfei Cao¹, Xionglin Luo², Xiaohong Song³

1 College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China;
2 Research Institute of Automation, China University of Petroleum, Beijing 102249, China;
3 State Grid Shandong Electric Power Company Zibo Power Supply Company, Zibo 255000, China

收稿日期:2016-07-15 修回日期:2016-08-18 出版日期:2017-09-28 发布日期:2017-10-11
通讯作者: Pengfei Cao,E-mail:cpfskdzdh@sina.com

Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor

Pengfei Cao¹, Xionglin Luo², Xiaohong Song³

1 College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China;
2 Research Institute of Automation, China University of Petroleum, Beijing 102249, China;
3 State Grid Shandong Electric Power Company Zibo Power Supply Company, Zibo 255000, China

Received:2016-07-15 Revised:2016-08-18 Online:2017-09-28 Published:2017-10-11

摘要/Abstract

摘要： Feasibility analysis of soft constraints for input and output variables is critical for model predictive control (MPC). When encountering the infeasible situation, some way should be found to adjust the constraints to guarantee that the optimal control law exists. For MPC integrated with soft sensor, considering the soft constraints for critical variables additionally makes it more complicated and difficult for feasibility analysis and constraint adjustment. Therefore, the main contributions are that a linear programming approach is proposed for feasibility analysis, and the corresponding constraint adjustment method and procedure are given as well. The feasibility analysis gives considerations to the manipulated, secondary and critical variables, and the increment of manipulated variables as well. The feasibility analysis and the constraint adjustment are conducted in the entire control process and guarantee the existence of optimal control. In final, a simulation case confirms the contributions in this paper.

关键词: Soft sensor, Model predictive control, Variable constraints, Feasibility analysis

Abstract: Feasibility analysis of soft constraints for input and output variables is critical for model predictive control (MPC). When encountering the infeasible situation, some way should be found to adjust the constraints to guarantee that the optimal control law exists. For MPC integrated with soft sensor, considering the soft constraints for critical variables additionally makes it more complicated and difficult for feasibility analysis and constraint adjustment. Therefore, the main contributions are that a linear programming approach is proposed for feasibility analysis, and the corresponding constraint adjustment method and procedure are given as well. The feasibility analysis gives considerations to the manipulated, secondary and critical variables, and the increment of manipulated variables as well. The feasibility analysis and the constraint adjustment are conducted in the entire control process and guarantee the existence of optimal control. In final, a simulation case confirms the contributions in this paper.

Key words: Soft sensor, Model predictive control, Variable constraints, Feasibility analysis

Pengfei Cao, Xionglin Luo, Xiaohong Song. Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor[J]. , 2017, 25(9): 1230-1237.

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Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor

Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor

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