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Constrained predictive control for multivariable systems with application to power systems
Author(s) -
Ordys Andrzej W.,
Kock Peter
Publication year - 1999
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/(sici)1099-1239(199909)9:11<781::aid-rnc436>3.0.co;2-4
Subject(s) - multivariable calculus , model predictive control , toolbox , matlab , computer science , power station , quadratic programming , control theory (sociology) , linear programming , control engineering , graphical user interface , interface (matter) , control (management) , mathematical optimization , algorithm , engineering , mathematics , artificial intelligence , electrical engineering , bubble , maximum bubble pressure method , parallel computing , programming language , operating system
Generalized predictive control (GPC) and dynamic performance predictive control (DPC) algorithms are introduced for industrial applications. Constraints on plant input rate, plant absolute input and plant absolute output can be implemented and are demonstrated on an application of these algorithms. A standard quadratic programming algorithm performs the calculation of the optimal control. A MATLAB/Simulink toolbox environment has been developed where controllers can be designed, linear and non‐linear plant models can be embedded, discrete‐ and continuous‐time loop parts can be mixed and simulation results can be managed and evaluated by graphical and statistical tools. This package utilises a graphical user interface. Finally, a case study design example is presented where a linear gas turbine model for power generation is examined with constrained GPC and DPC, and the advantages and drawbacks of the approach are the discussed. Copyright © 1999 John Wiley & Sons, Ltd.