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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F06%3APU62059" target="_blank" >RIV/00216305:26220/06:PU62059 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Model Predictive Controller Based on Neural Network Used for Multi-Dimensional Control
Original language description
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 predictivve 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).
Czech name
Prediktivní vícerozměrný regulátor s neuronovou sítí.
Czech description
Článek prezentuje vícerozměrové prediktivní řízení teplovzdušného modelu s identifikací založenou na neuronové síti.

Project
<a href="/en/project/GA102%2F06%2F1132" target="_blank" >GA102/06/1132: Softcomputing in Control</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2006
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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