Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/85618

Improved Design of Constrained Model Predictive Tracking Control for Batch Processes Against Unknown Uncertainties

Authors Wu, Sheng
Jin, Qibing
Zhang, Ridong HKUST affiliated (currently or previously)
Zhang, Junfeng
Gao, Furong View this author's profile
Issue Date 2017
Source ISA Transactions , v. 69, July 2017, p. 273-280
Summary In this paper, an improved constrained tracking control design is proposed for batch processes under uncertainties. A new process model that facilitates process state and tracking error augmentation with further additional tuning is first proposed. Then a subsequent controller design is formulated using robust stable constrained MPC optimization. Unlike conventional robust model predictive control (MPC), the proposed method enables the controller design to bear more degrees of tuning so that improved tracking control can be acquired, which is very important since uncertainties exist inevitably in practice and cause model/plant mismatches. An injection molding process is introduced to illustrate the effectiveness of the proposed MPC approach in comparison with conventional robust MPC. © 2017 ISA
Subjects
ISSN 0019-0578
1879-2022
Language English
Format Article
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