文章基本信息

标题：Improved offset-free model predictive control utilizing learned model-plant mismatch map
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作者：Sang Hwan Son ; Jong Woo Kim ; Tae Hoon Oh 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：7
页码：792-797
DOI：10.1016/j.ifacol.2022.07.541
语种：English
出版社：Elsevier
摘要：AbstractThe requirement for a framework that effectively overcomes the limitation of model-based and data-driven control strategies by combining both methods continues to grow. In this study, we propose an approach that learns the model-plant mismatch map and utilizes it based on the offset-free model predictive control (MPC). Specifically, the mismatch map is learned via general regression neural network (GRNN) that has been applied in broad range of fields based on the data from the process, and then the learned mismatch information is provided to the MPC system. In addition, since the approximated mismatch map via GRNN cannot be perfect, an additional supplementary disturbance estimator is utilize to ensure the zero-offset tracking property. Finally, the learned and supplementary disturbance signals are applied to the target problem and the optimal control problem based on the offset-free MPC framework. The effectiveness of the proposed combined model-based and data driven framework is demonstrated by closed-loop simulation. The result shows that the proposed framework can improve the closed-loop tracking performance by utilizing both the learned mismatch information from GRNN and the stabilizing property of the supplementary disturbance estimator.
关键词：KeywordsModel predictive controlgeneral regression neural networkmodel-plant mismatchoffset-free tracking