Constrained long-range predictive control based on artificial neural networks

A long-range predictive control strategy using artificial neural networks ( ANNs) is represented. Both unconstrained and constrained control problems are considered. In this control scheme a recurrent ANN and a multilayer feedforward ANN are used. The recurrent ANN is used as a multi-step ahead pred...

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Bibliographic Details
Published in:International journal of systems science 1997-07, Vol.28 (12), p.1211-1226
Main Authors: NAJIM, K., RUSNAK, A., MESZAROS, A., FIKAR, M.
Format: Article
Language:English
Subjects:
Applied sciences
Biological and medical sciences
Bioreactors
Biotechnology
Computer science
control theory
systems
Control system synthesis
Control theory. Systems
Exact sciences and technology
Fermented food industries
Food industries
Fundamental and applied biological sciences. Psychology
Methods. Procedures. Technologies
Other fermented food industries
Various methods and equipments
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Summary:A long-range predictive control strategy using artificial neural networks ( ANNs) is represented. Both unconstrained and constrained control problems are considered. In this control scheme a recurrent ANN and a multilayer feedforward ANN are used. The recurrent ANN is used as a multi-step ahead predictor. For training this network the backpropagation through the time is used. The control action is provided by the multilayer feedforward ANN which uses the predictions of the output of the process to be controlled. The weights of this ANN are estimated at each control step using a stochastic approximation ( SA) algorithm by minimizing a quadratic control objective which is based on a series of the future predictions and future control actions, and by preventing violations of process constraints. To demonstrate the feasibility and the performance of this control scheme, a continuous biochemical reactor and a fixed bed tubular chemical reactor are chosen as realistic nonlinear case studies. Simulation results demonstrate the usefulness and the robustness of this predictive control algorithm.
ISSN:0020-7721
1464-5319
DOI:10.1080/00207729708929479