Nonlinear predictive control based on neural multi-models

Maciej Ławryńczuk; Piotr Tatjewski

International Journal of Applied Mathematics and Computer Science (2010)

  • Volume: 20, Issue: 1, page 7-21
  • ISSN: 1641-876X

Abstract

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This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.

How to cite

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Maciej Ławryńczuk, and Piotr Tatjewski. "Nonlinear predictive control based on neural multi-models." International Journal of Applied Mathematics and Computer Science 20.1 (2010): 7-21. <http://eudml.org/doc/207979>.

@article{MaciejŁawryńczuk2010,
abstract = {This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.},
author = {Maciej Ławryńczuk, Piotr Tatjewski},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {process control; model predictive control; neural networks; optimisation; linearisation},
language = {eng},
number = {1},
pages = {7-21},
title = {Nonlinear predictive control based on neural multi-models},
url = {http://eudml.org/doc/207979},
volume = {20},
year = {2010},
}

TY - JOUR
AU - Maciej Ławryńczuk
AU - Piotr Tatjewski
TI - Nonlinear predictive control based on neural multi-models
JO - International Journal of Applied Mathematics and Computer Science
PY - 2010
VL - 20
IS - 1
SP - 7
EP - 21
AB - This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.
LA - eng
KW - process control; model predictive control; neural networks; optimisation; linearisation
UR - http://eudml.org/doc/207979
ER -

References

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