Methodology for Detecting Model鈥揚lant Mismatches Affecting Model Predictive Control Performance

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• 作者：Viviane Botelho ; Jorge Ot谩vio Trierweiler ; Marcelo Farenzena ; Ricardo Duraiski
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2015
• 出版时间：December 9, 2015
• 年：2015
• 卷：54
• 期：48
• 页码：12072-12085
• 全文大小：991K
• ISSN：1520-5045

文摘

The model quality for a model predictive control (MPC) is critical for the control loop performance. Thus, assessing the effect of model鈥損lant mismatch (MPM) is fundamental for performance assessment and monitoring the MPC. This paper proposes a method for evaluating model quality based on the investigation of closed-loop data and the nominal output sensitivity function, which facilitates the assessment procedure for the actual closed-loop performances. The effectiveness of the proposed method is illustrated by a multivariable case study, considering linear and nonlinear plants.