Neural Network Predictive Control
Artificial Neural Networks (ANN) can and are succesively used as process
models. In this research neural networks are used as nonlinear models in
predictive control.
In the first approach, direct substitution of ANN model in GPC is used.
This is based on idea that GPC is not related to any particular process
model, but it rather needs only two process characteristics: step and free
responses. These are obtained from ANN.
Next, a more complicated scheme is used where one ANN is used as a model
while the second forms predictive controller  it is learned online. Thus
the optimised variables are not future control increments as usual, but
ANN weights. After some time, the controller ANN is able to control online
without further learning. To make online learning possible, stochastic
approximation is used in optimisation.
Neural networks are also used in predictive control where the optimiser
is based on Iterative Dynamic Programming (IDP)  a method for dynamic
optimisation. The whole is used in the receding horizon framework. As ANN
are fast predictors, online implementation of the algorithm is possible.
Journals

Rusnák, A., Fikar, M., Latifi, M. A., Mészáros, A.:
Receding Horizon Iterative Dynamic Programming with Discrete Time Models.
Computers chem. Engng., 25(1), 161167, 2001.
Abstract

Rusnák, A., Fikar, M., Mészáros, A.: Receding Horizon
Control Using Modified Iterative Dynamic Programming and Neural Network
Models. Computers chem. Engng., 23, S297S300, 1999.
Abstract

Najim, K., Rusnák, A., Mészáros, A., Fikar, M.: Constrained
LongRange Predictive Control Based on Artificial Neural Networks. Int.
J. Systems Science, 28 (12), 1211  1226, 1997. Abstract

Rusnák, A., Fikar, M., Najim, K., Mészáros, A.: Generalized
Predictive Control Based on Neural Networks. Neural Processing Letters,
4(2), 107112, 1996. Abstract
Conferences

Rusnák, A., Fikar, M., Mészáros, A.: Receding Horizon
Control Using Modified Iterative Dynamic Programming and Neural Network
Models. Computers chem. Engng. / ESCAPE 9, 23, S297S300, 1999. Abstract,
Poster.PDF
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