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A critical study of multivariable self‐tuning algorithms for distillation control
Author(s) -
Radhakrishnan Thota K.,
Gangiah Kota
Publication year - 1991
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.270140607
Subject(s) - multivariable calculus , fractionating column , control theory (sociology) , controller (irrigation) , self tuning , distillation , control variable , algorithm , noise (video) , mimo , forgetting , computer science , control engineering , engineering , pid controller , control (management) , temperature control , artificial intelligence , chemistry , machine learning , computer network , agronomy , channel (broadcasting) , linguistics , philosophy , organic chemistry , image (mathematics) , biology
A control algorithm which has been acclaimed as the best algorithm for a real system may not be the best algorithm for a different real system. Therefore, various self‐tuning algorithms for real distillation columns have been evaluated, in order to compare their performances. A variable forgetting factor algorithm is modified using a filter which permits the employment of one instead of two covariance matrices for distillation control. A cautious self‐tuning control of SISO system is extended to MIMO system of distillation control. Multivariable self‐tuning regulator, multivariable self‐tuning controller and multivariable cautious self‐tuning controller are implemented with modified variable forgetting factor for linear transfer function model, Waller et al. column, and rigorous non‐linear model, Wood and Berry column. For distillation control, a multivariable cautious self‐tuning algorithm with modified variable forgetting factor is much simpler than earlier reported algorithms. This has produced better results and demonstrated its effectiveness, even in the presence of noise when other adaptive controllers give unsatisfactory performance.