A new scheme combining neural feedforward control with model-predictive control

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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 on-line 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 random input patterns.
Publisher
Wiley-Blackwell
Issue Date
1992
Language
English
Article Type
Article
Keywords

SYSTEMS

Citation

AICHE JOURNAL, v.38, no.2, pp.193 - 200

ISSN
0001-1541
URI
http://hdl.handle.net/10203/10849
Appears in Collection
CBE-Journal Papers(저널논문)
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