Predictive control using neural networks

The predictive control of nonlinear systems has recently been the subject of several research works and several algorithms, in particular those using fuzzy logic and neural networks. In this paper, we present a method for unconstrained predictive control of nonlinear systems. This method, uses a sta...

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Bibliographic Details
Published in2009 35th Annual Conference of IEEE Industrial Electronics pp. 1702 - 1705
Main Authors Kara, K., Hadjili, M.L., Hemsas, K.E., Missoum, T.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Continuous-stirred tank reactor
Cost function
Error correction
Neural networks
Nonlinear systems
Prediction algorithms
Predictive control
Predictive models
Signal analysis
Trajectory
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ISBN9781424446483
1424446481
ISSN1553-572X
DOI10.1109/IECON.2009.5414824

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Summary:The predictive control of nonlinear systems has recently been the subject of several research works and several algorithms, in particular those using fuzzy logic and neural networks. In this paper, we present a method for unconstrained predictive control of nonlinear systems. This method, uses a static neural network as a prediction model and is based on the idea of dividing the predicted output into it's free and forced parts. Such division of the predicted output allows obtaining analytically the sequence of control signals. We use this technique for the predictive control of a Continuous Stirred Tank Reactor (CSTR).
ISBN:9781424446483
1424446481
ISSN:1553-572X
DOI:10.1109/IECON.2009.5414824