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Multivariable process and prediction models in predictive control—A unified approach
Author(s) -
De Vries R. A. J.,
Verbruggen H. B.
Publication year - 1994
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.4480080304
Subject(s) - multivariable calculus , control theory (sociology) , model predictive control , transfer function , process (computing) , noise (video) , controller (irrigation) , computer science , mathematics , engineering , control engineering , control (management) , artificial intelligence , agronomy , electrical engineering , image (mathematics) , biology , operating system
In this paper multivariable generalizations of the SISO unified process model (UPCM) and unified prediction model (UPM) used in the SISO unified predictive controller are presented. the necessary assumptions regarding the multivariable UPCM (MUPCM) are established. the properties of the multivariable UPM (MUPM) are examined and the conditions for a zero steady state prediction error are established. The MUPCM has a general noise model and unites all multivariable, discrete, linear process models in transfer function form. the MUPM uses an output filter which unites all relevant output filters used in predictive controllers. Furthermore, the elements of the output can be predicted over different horizons. the MUPCM and MUPM and the algorithm used to calculate the MUPM are chosen such that the predictions of the outputs can be calculated as fast as possible, not only for the general model, but also for special cases such as ARX, ARIMAX, Box‐Jenkins and FIR models.