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Nonlinear predictive control using multi‐rate sampling
Author(s) -
Bequette B. Wayne
Publication year - 1991
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450690116
Subject(s) - control theory (sociology) , continuous stirred tank reactor , model predictive control , nonlinear system , sampling (signal processing) , process control , compensation (psychology) , trajectory , control variable , internal model , process (computing) , mathematics , computer science , engineering , statistics , control (management) , artificial intelligence , physics , quantum mechanics , chemical engineering , operating system , psychology , filter (signal processing) , astronomy , psychoanalysis , computer vision
A nonlinear predictive control (NLPC) strategy based on a nonlinear, lumped parameter model of the process is developed in this paper. A constrained optimization approach is used to estimate unmeasured state variables and load disturbances. Additional model/process mismatch is handled by using an additive output term which is equivalent to the Internal Model Control approach. Similar to linear predictive control methods, an optimal sequence of future control moves is determined in order to minimize an objective function based on a desired output trajectory, subject to manipulated variable constraints (absolute and velocity). Deadtime is explicitly included in the model formulation, giving NLPC the same deadtime compensation feature of linear model‐predictive techniques. The multi‐rate sampling nature of most chemical processes is also used to improve estimates of process disturbances. Infrequent composition measurements in conjunction with frequent temperature measurements are used to improve the “inferential” control of the composition in a continuous flow stirred tank reactor (CSTR).