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Optimization algorithms for bilinear model–based predictive control problems
Author(s) -
Bloemen H. H. J.,
van den Boom T. J. J.,
Verbruggen H. B.
Publication year - 2004
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.10122
Subject(s) - bilinear interpolation , model predictive control , linearization , nonlinear system , mathematical optimization , algorithm , quadratic programming , mathematics , optimization problem , sequence (biology) , quadratic equation , nonlinear programming , optimal control , computer science , control theory (sociology) , control (management) , artificial intelligence , statistics , physics , geometry , quantum mechanics , biology , genetics
Model‐based predictive control (MPC) for discrete‐time bilinear state–space models is considered. The optimization problem of the bilinear MPC algorithm is nonlinear in general. It is demonstrated that the structural properties of the bilinear state–space model provide a way to formulate the nonlinear optimization problem as a sequence of quadratic programming problems that exactly represent the original objective function. The proposed optimization algorithm is compared to one that is based on a linearization about an input trajectory. To benefit from the advantages of both algorithms, a hybrid algorithm is proposed, which outperforms the other two in most cases. The applicability of the proposed bilinear MPC algorithm is demonstrated on a polymerization process. 2004 American Institute of Chemical Engineers AIChE J, 50:1453–1461, 2004