An on-line constraint softening strategy to guarantee the feasibility of dynamic controller in double-layered MPC

Hongguang Pan; Weimin Zhong; Zaiying Wang

doi:10.1016/j.cjche.2017.06.010

Your Location：

Home >

Browse articles >

An on-line constraint softening strategy to guarantee the feasibility of dynamic controller in double-layered MPC

第25届中国过程控制会议专栏 | Updated：2026-01-06

- An on-line constraint softening strategy to guarantee the feasibility of dynamic controller in double-layered MPC
- Chinese Journal of Chemical Engineering Vol. 25, Issue 12, Pages: 1805-1811(2017)
- Affiliations：
  
  Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2017.06.010
  CLC：
- Published：2017
- Accepted：
Scan QR Code
Hongguang Pan, Weimin Zhong, Zaiying Wang. An on-line constraint softening strategy to guarantee the feasibility of dynamic controller in double-layered MPC[J]. Chin.J.Chem.Eng., 2017, 25(12): 1805-1811.
DOI：

Hongguang Pan, Weimin Zhong, Zaiying Wang. An on-line constraint softening strategy to guarantee the feasibility of dynamic controller in double-layered MPC[J]. Chin.J.Chem.Eng., 2017, 25(12): 1805-1811. DOI： 10.1016/j.cjche.2017.06.010.

摘要

Abstract

A double-layered model predictive control (MPC)

which is composed of a steady-state target calculation (SSTC) layer and a dynamic control layer

is a prevailing hierarchical structure in industrial process control. Based on the reason analysis of the dynamic controller infeasibility

an on-line constraints softening strategy is given. At first

a series of regions of attraction (ROA) of the dynamic controller is calculated according to the softened constraints; then a minimal ROA containing the current state is chosen and the corresponding softened constraint is adopted by the dynamic controller. Note that

the above measures are performed on-line because the centers of the above ROA are the steady-state targets calculated at each instant. The effectiveness of the presented strategy is illustrated through two examples.

关键词

Keywords

references

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Robust particle filtering-based nonlinear model predictive control: Application to PEMFC process

Design and control of an extractive distillation process for separating isopropanol and water with side-stream extraction

A fuzzy compensation-Koopman model predictive control design for pressure regulation in proten exchange membrane electrolyzer

Uncertainty and disturbance estimator-based model predictive control for wet flue gas desulphurization system

Process design and intensification of multicomponent azeotropes special distillation separation via molecular simulation and system optimization

Related Author

Lei Xie

Fanda Pan

Qi Zhang

Hui Tian

Haoyang Xu

Mengqi Li

Lei Xie

Haokun Xiong

Related Institution

Institute of Intelligence Science and Engineering, Shenzhen Polytechnic University

Technical Center of Zhejiang China Tobacco Industrial Co. Ltd.

BYD Automobile Industry Co., Ltd.

State Key Laboratory of Industrial Control Technology, Zhejiang University

College of Chemistry and Chemical Engineering, Yantai University

⁰