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Internal model control design for input‐constrained multivariable processes
Author(s) -
Adegbege Ambrose A.,
Heath William P.
Publication year - 2011
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.12540
Subject(s) - control theory (sociology) , multivariable calculus , internal model , stability (learning theory) , norm (philosophy) , controller (irrigation) , model predictive control , actuator , quadratic equation , engineering , mathematics , mathematical optimization , control (management) , computer science , control engineering , agronomy , geometry , electrical engineering , artificial intelligence , machine learning , law , political science , biology
Multivariable plants under input constraints such as actuator saturation are liable to performance deterioration due to control windup and directionality change. A two‐stage internal model control (IMC) antiwindup design for open loop stable plants is presented. The design is based on the solution of two low‐order quadratic programs at each time step, which addresses both transient and steady‐state behaviors of the system. For analyzing the robust stability of such systems against any infinity‐norm bounded uncertainty, stability test have also been developed. In particular, we note that the controller input‐output mappings satisfy certain integral quadratic constraints. Simulated examples show that the two‐stage IMC has superior performance when compared with other existing optimization‐based antiwindup methods. The stability test is illustrated for a plant with left matrix fraction uncertainty. A scenario where the proposed two‐stage IMC competes favorably with a long prediction horizon model predictive control is described. © 2011 American Institute of Chemical Engineers AIChE J, 2011