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Modeling and control of multivariable processes: Dynamic PLS approach
Author(s) -
Lakshminarayanan S.,
Shah Sirish L.,
Nandakumar K.
Publication year - 1997
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690430916
Subject(s) - multivariable calculus , projection (relational algebra) , feed forward , control theory (sociology) , multivariate statistics , computer science , process (computing) , nonlinear system , process control , system dynamics , partial least squares regression , control engineering , control (management) , algorithm , artificial intelligence , engineering , machine learning , physics , quantum mechanics , operating system
The issue of modeling and control of multivariable chemical process systems using the dynamic version of a popular multivariate statistical technique, namely, projection to latent structures (partial least squares or PLS) is addressed. Discrete input‐output data are utilized to construct a projection‐based dynamic model that captures the dominant features of the process under study. The structure of the resulting model enables the synthesis of a multiloop control system. In addition, the design of feedforward control for multivariable systems using the dynamic PLS framework is also presented. Three case studies are used to illustrate the modeling and control of multivariable linear and nonlinear systems using the suggested approach.