Approximate models for nonlinear process control | Zendy

Sentoni G. | Zendy; Agameni O. | Zendy; Desages A. | Zendy; Romagnoli J. | Zendy

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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.

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