Detail publikace

Model Predictive Controller Based on Neural Network Used for Multi-Dimensional Control

P. Nepevný, P. Pivoňka

Originální název

Model Predictive Controller Based on Neural Network Used for Multi-Dimensional Control

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

This paper presents a solution of multi-dimensional Model Predictive Control (MPC) based on feed-forward Neural Network (NN) model. Autoregressive NN model with back-propagation learning algorithm is used for system output prediction. It is able to observe system changes and adapt itself, therefore adaptive MPC controller is obtained. MPC is a kind of optimal controller, because a control action is always optimal according to the given criterion. There is shown, how to create multi-dimensional predictive controller. Possibilities of multi-dimensional MPC were tested on laboratory physical model – hot-air tunnel. Two quantities of hot-air tunnel were controlled – the air flow and the temperature. The algorithm was implemented in MATLAB-Simulink and tested on a physical model. Communication between PC and hot-air tunnel was provided by PLC (connected via Ethernet).

Klíčová slova

Predictive Controllers, Neural Networks for Identification, Multi-Dimensional Control

Autoři

P. Nepevný, P. Pivoňka

Rok RIV

2006

Vydáno

2. 10. 2006

Nakladatel

Rektor der Hochschule Zittau/Gorlitz

Místo

Zittau

ISBN

3-9808089-8-X

Kniha

East West Fuzzy Colloquium

Strany od

69

Strany do

74

Strany počet

6

BibTex

@inproceedings{BUT19681,
  author="Petr {Nepevný} and Petr {Pivoňka}",
  title="Model Predictive Controller Based on Neural Network Used for Multi-Dimensional Control",
  booktitle="East West Fuzzy Colloquium",
  year="2006",
  pages="6",
  publisher="Rektor der Hochschule Zittau/Gorlitz",
  address="Zittau",
  isbn="3-9808089-8-X"
}