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P. Nepevný, P. Pivoňka
Original Title
Model Predictive Controller Based on Neural Network Used for Multi-Dimensional Control
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
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).
English abstract
Keywords
Predictive Controllers, Neural Networks for Identification, Multi-Dimensional Control
Key words in English
Authors
Released
02.10.2006
Publisher
Rektor der Hochschule Zittau/Gorlitz
Location
Zittau
ISBN
3-9808089-8-X
Book
East West Fuzzy Colloquium
Pages from
69
Pages count
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" }