Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance

doi:10.1016/j.cjche.2016.05.041

›› 2016, Vol. 24 ›› Issue (10): 1431-1441.DOI: 10.1016/j.cjche.2016.05.041

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

Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance

Baocang Ding, Hongguang Pan

Ministry of Education Key Lab For Intelligent Networks and Network Security, Department of Automation, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China

收稿日期:2015-05-27 修回日期:2015-10-22 出版日期:2016-10-28 发布日期:2016-11-19
通讯作者: Baocang Ding,E-mail address:baocangding@126.com.
基金资助:
Supported by the National High Technology Research and Development Program of China (2014AA041802) and the National Natural Science Foundation of China (61573269).

Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance

Baocang Ding, Hongguang Pan

Ministry of Education Key Lab For Intelligent Networks and Network Security, Department of Automation, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China

Received:2015-05-27 Revised:2015-10-22 Online:2016-10-28 Published:2016-11-19
Supported by:
Supported by the National High Technology Research and Development Program of China (2014AA041802) and the National Natural Science Foundation of China (61573269).

摘要/Abstract

摘要： The output feedback model predictive control (MPC), for a linear parameter varying (LPV) process system including unmeasurable model parameters and disturbance (all lying in known polytopes), is considered. Some previously developed tools, including the norm-bounding technique for relaxing the disturbance-related constraint handling, the dynamic output feedback law, the notion of quadratic boundedness for specifying the closed-loop stability, and the ellipsoidal state estimation error bound for guaranteeing the recursive feasibility, are merged in the control design. Some previous approaches are shown to be the special cases. An example of continuous stirred tank reactor (CSTR) is given to show the effectiveness of the proposed approaches.

关键词: Model predictive control, Process systems, Stability, Recursive feasibility, Uncertainty, Norm-bounding technique

Abstract: The output feedback model predictive control (MPC), for a linear parameter varying (LPV) process system including unmeasurable model parameters and disturbance (all lying in known polytopes), is considered. Some previously developed tools, including the norm-bounding technique for relaxing the disturbance-related constraint handling, the dynamic output feedback law, the notion of quadratic boundedness for specifying the closed-loop stability, and the ellipsoidal state estimation error bound for guaranteeing the recursive feasibility, are merged in the control design. Some previous approaches are shown to be the special cases. An example of continuous stirred tank reactor (CSTR) is given to show the effectiveness of the proposed approaches.

Key words: Model predictive control, Process systems, Stability, Recursive feasibility, Uncertainty, Norm-bounding technique

Baocang Ding, Hongguang Pan. Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance[J]. , 2016, 24(10): 1431-1441.

参考文献

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Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance

Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance

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