Performance Assessment of Model Pedictive Control for Variability and Constraint Tuning

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• 作者：Fangwei Xu ; Biao Huang ; Seyi Akande
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2007
• 出版时间：February 14, 2007
• 年：2007
• 卷：46
• 期：4
• 页码：1208 - 1219
• 全文大小：401K
• 年卷期：v.46,no.4(February 14, 2007)
• ISSN：1520-5045

文摘

Multivariate controller performance assessment (MVPA) has been developed over the last several years, butits application in advanced model predictive control (MPC) has been limited mainly due to issues associatedwith comparability of the variance control objective and that of MPC applications. MPC has been proven asone of the most effective advanced process control (APC) strategies to deal with multivariable constrainedcontrol problems with an ultimate objective toward economic optimization. Any attempt to evaluate MPCperformance should therefore consider constraints and economic performance. In this work, we show that thevariance based performance assessment may be transferred to performance assessment of MPC applications.The MPC economic performance can be evaluated by solving benefit potentials through either variabilityreduction of quality output variables or tuning of constraints. Algorithms for MPC performance assessmentand constraint/variance tuning guidelines are developed through linear matrix inequalities (LMIs) using routineoperating process data plus the process steady-state gain matrix. The proposed approach for MPC economicperformance evaluation is illustrated and verified via a simulation example of an MPC application as well asa pilot-scale experiment.