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

Pengfei Cao; Xionglin Luo; Xiaohong Song

doi:10.1016/j.cjche.2016.08.018

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

Process Systems Engineering and Process Safety | Updated：2026-01-06

- 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
- 中国化学工程学报（英文版） 2017年25卷第9期页码：1230-1237
- Affiliations：
  
  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology,Qingdao,China,266590
  2. Research Institute of Automation, China University of Petroleum,Beijing,China,102249
  3. State Grid Shandong Electric Power Company Zibo Power Supply Company,Zibo,China,255000
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2016.08.018
  中图分类号：
- 纸质出版：2017
- Accepted：
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Pengfei Cao, Xionglin Luo, Xiaohong Song. Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor[J]. 中国化学工程学报（英文版）, 2017,25(9):1230-1237.

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.
Pengfei Cao, Xionglin Luo, Xiaohong Song. Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor[J]. 中国化学工程学报（英文版）, 2017,25(9):1230-1237. DOI： 10.1016/j.cjche.2016.08.018.

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. DOI： 10.1016/j.cjche.2016.08.018.

摘要

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.

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