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Approximate models for nonlinear process control
Author(s) -
Sentoni G.,
Agameni O.,
Desages A.,
Romagnoli J.
Publication year - 1996
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.690420813
Subject(s) - nonlinear system , laguerre polynomials , perceptron , control theory (sociology) , fractionating column , binary number , model predictive control , mathematics , computer science , invariant (physics) , process (computing) , distillation , algorithm , artificial neural network , control (management) , artificial intelligence , mathematical analysis , chemistry , physics , organic chemistry , arithmetic , quantum mechanics , mathematical physics , operating system
A methodology is presented to obtain approximate models from input–output data, particularly oriented to implement a model‐predictive control scheme. Causal, time‐invariant nonlinear discrete systems with a certain type of continuity condition called fading memory are dealt with. To synthesize the nonlinear model a finite‐dimensional linear dynamic part (discrete Laguerre polynomials) is used, followed by a nonlinear nonmemory map (single hidden‐layer perceptron). Results of the application to approximate and control a binary distillation column are presented.