文章基本信息

标题：Assessment of Model-Plant Mismatch by the Nominal Sensitivity Function for Unconstrained MPC
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作者：Viviane Rodrigues Botelho ; Jorge Otávio Trierweiler ; Marcelo Farenzena 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：8
页码：753-758
DOI：10.1016/j.ifacol.2015.09.059
语种：English
出版社：Elsevier
摘要：AbstractModel Predictive Control (MPC) is a class of control systems which use a dynamic process model to predict the best future control actions based on past information. Thus, a representative process model is a key factor for its correct performance. Therefore, the investigation of model-plant-mismatch effect is very important issue for MPC performance assessment, monitoring, and diagnosis. This paper presents a method for model quality evaluation based on the investigation of closed-loop data and the nominal complementary sensitivity function. The proposed approach ensures that the MPC tuning is taken into account in the evaluation of the model quality. A SISO case study is analyzed and the results show the effectiveness of the method.
关键词：KeywordsModel Predictive ControlModel Plant MismatchSensitivity FunctionControl Performance Assessment