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Multimodel‐based minimum bias control of a benchmark paper machine process
Author(s) -
Liu D. Y.,
Fisher D. G.,
Shah S. L.,
Liu X.
Publication year - 1997
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.5450750123
Subject(s) - benchmark (surveying) , robustness (evolution) , computer science , scheme (mathematics) , range (aeronautics) , process (computing) , control (management) , control theory (sociology) , model predictive control , artificial intelligence , mathematics , engineering , mathematical analysis , biochemistry , chemistry , geodesy , aerospace engineering , gene , geography , operating system
This paper presents a multimodel‐based minimum bias (MB), long‐range predictive control algorithm that minimizes the bias in the output using a multiobjective optimization method. To enhance the robustness of the control system, the MB control scheme is extended to the multimodel‐ or multicontroller‐ based MB control scheme, which establishes several models simultaneously. Models with relatively small prediction errors are selected to form a group of acceptable models. The final control is chosen to be the weighted average of the MB controls. Models with significant or large errors are reinitialized. The proposed MB control scheme is evaluated by application to a paper machine benchmark problem and its performance is compared with that of other controllers.