Neural Model Predictive Control for Nonlinear Chemical Processes

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A neural model predictive contrl strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrainted nonlinear optimization approach using successive quadratic programming combined with a neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller(NMPC) shows good performance and robustness.
Publisher
The Society of Chemical Engineers
Issue Date
1993
Keywords

Neural Network; Model Predictive Control; Identification; Intelligent Control; Nonlinear Control

Citation

Journal of Chemical Engineering of Japan, Vol.26 , No.4, pp.347-354

ISSN
0021-9592
DOI
10.1252/jcej.26.347
URI
http://hdl.handle.net/10203/25275
Appears in Collection
CBE-Journal Papers(저널논문)

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