A comparison of implementation strategies for MPC

Four quadratic programming (QP) formulations of model predictive control (MPC) are compared with regards to ease of formulation, memory requirement, and numerical properties. The comparison is based on two example processes: a paper machine model, and a model of the Tennessee Eastman challenge proce...

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Bibliographic Details
Published in:Modeling, identification and control identification and control, 2005, Vol.26 (1), p.39-50
Main Authors: LIE, Bernt, DIEZ, Marta Duenas, HAUGE, Tor Anders
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Control theory. Systems
Exact sciences and technology
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Summary:Four quadratic programming (QP) formulations of model predictive control (MPC) are compared with regards to ease of formulation, memory requirement, and numerical properties. The comparison is based on two example processes: a paper machine model, and a model of the Tennessee Eastman challenge process; the number of free variables range from 150-1400. Five commercial QP solvers are compared. Preliminary results indicate that dense solvers still are the most efficient, but sparse solvers hold great promise.
ISSN:0332-7353
1890-1328
DOI:10.4173/mic.2005.1.3