|
|
Digital Library of the
European Council for Modelling and Simulation |
|
Title: |
Neural Network Predictive Control Of A Chemical Reactor |
|
Authors: |
Anna Vasičkaninová,
Monika Bakońová |
|
Published in:
|
(2009).ECMS
2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel
Pedrera. European Council for Modeling and
Simulation. doi:10.7148/2009 ISBN: 978-0-9553018-8-9 23rd
European Conference on Modelling and Simulation, Madrid, June
9-12, 2009 |
|
Citation
format: |
Vasickaninova, A., & Bakosova,
M. (2009). Neural Network Predictive Control Of A Chemical Reactor. ECMS 2009
Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera (pp.
563-569). European Council for Modeling and Simulation. doi:10.7148/2009-0563-0569 |
|
DOI: |
http://dx.doi.org/10.7148/2009-0563-0569 |
|
Abstract: |
Model
Predictive Control (MPC) refers to a class of algorithms that compute a sequence of manipulated variable
adjustments in order to optimize the future behaviour
of a plant. MPC technology can now be found in a wide variety of application
areas. The neural network predictive controller that is discussed in this
paper uses a neural network model of a nonlinear plant to predict future
plant performance. The controller calculates the control input that will
optimize plant performance over a specified future time horizon. In the
paper, simulation of the neural network based predictive control for the
continuous stirred tank reactor is presented. The simulation results are
compared with fuzzy and PID control. |
|
Full
text: |
|
