A neural linearizing control scheme for nonlinear chemical processes

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A new neural linearizing control scheme (NLCS) is proposed for controlling nonlinear chemical processes. In the proposed NLCS a radial basis function (RBF) network is used to linearize the relation between the output of the linear controller and the process output. The learning of the RBF network proceeds adaptively to minimize the difference between the output of the pre-defined linear reference model and the process output, After the neural network is fully trained, the apparent dynamics of the process becomes linear. The determination of the linear reference model and the heuristics about avoiding the effects of the unmeasured disturbances are considered. Simulation studies on a continuous stirred tank reactor and a pH process are carried out to evaluate the performance of the proposed control scheme. Copyright (C) 1996 Elsevier Science Ltd
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
1997
Language
English
Article Type
Article
Keywords

MODEL-PREDICTIVE CONTROL; NETWORKS; SYSTEMS

Citation

COMPUTERS & CHEMICAL ENGINEERING, v.21, no.2, pp.187 - 200

ISSN
0098-1354
DOI
10.1016/0098-1354(95)00261-8
URI
http://hdl.handle.net/10203/67816
Appears in Collection
CBE-Journal Papers(저널논문)EE-Journal Papers(저널논문)
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