Optimization algorithms for bilinear model-based predictive control problems

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 nonline...

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Bibliographic Details
Published in:AIChE journal 2004-07, Vol.50 (7), p.1453-1461
Main Authors: Bloemen, H. H. J., van den Boom, T. J. J., Verbruggen, H. B.
Format: Article
Language:English
Subjects:
Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization
Applied sciences
bilinear
Chemical engineering
discrete time
Exact sciences and technology
nonlinear optimization
Optimization algorithms
Organic polymers
Physicochemistry of polymers
Polymerization
Predictive control
Preparation, kinetics, thermodynamics, mechanism and catalysts
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Summary: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
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.10122