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A new scheme combining neural feedforward control with model‐predictive control
Author(s) -
Lee Moonyong,
Park Sunwon
Publication year - 1992
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.690380204
Subject(s) - feed forward , control theory (sociology) , model predictive control , controller (irrigation) , artificial neural network , fractionating column , generalization , computer science , scheme (mathematics) , feedforward neural network , nonlinear system , control engineering , process (computing) , control (management) , engineering , distillation , artificial intelligence , mathematics , chemistry , mathematical analysis , agronomy , physics , organic chemistry , quantum mechanics , biology , operating system
Abstract A new control scheme is presented for feedforward control of unknown disturbances in the model‐predictive control (MPC) scheme. In this control scheme, a neural network is connected in parallel with the MPC controller and trained online by minimizing the MPC controller output corresponding to the unmodeled effect. It is applied to distillation column control and nonlinear reactor control to illustrate its effectiveness. The result shows that the neural feedforward controller can cope well with strong interactions, time delays, nonlinearities, and process/model mismatch. The controller also offers such advantages as fault tolerance, generalization capability by interpolation, and learning capability by randdom input patterns.