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Performance assessment of constrained model predictive control systems
Author(s) -
Ko ByungSu,
Edgar Thomas F.
Publication year - 2001
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690470613
Subject(s) - model predictive control , variance (accounting) , minimum variance unbiased estimator , controller (irrigation) , horizon , control theory (sociology) , process (computing) , mathematical optimization , computer science , upper and lower bounds , control (management) , mathematics , statistics , artificial intelligence , economics , mathematical analysis , geometry , accounting , mean squared error , agronomy , biology , operating system
A constrained minimum variance controller is derived based on a moving horizon approach that explicitly accounts for hard constraints on process variables. A procedure for the performance assessment of constrained model predictive control systems is then developed based on the constrained minimum variance controller. The performance bound computed using the proposed moving horizon approach converges to the unconstrained minimum variance performance bound when the constraints on process variables become inactive. The utility of the proposed method in the performance assessment of constrained model predictive control systems is demonstrated through a simulated example.