Multi-Dimensional Predictive Control of Hot-Air Tunnel Using a Neural Network Modell

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F05%3APU51435" target="_blank" >RIV/00216305:26220/05:PU51435 - 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
Multi-Dimensional Predictive Control of Hot-Air Tunnel Using a Neural Network Modell
Original language description
Model-based predictive controller (MPC) is a kind of optimal controller based on system model. Model is used for prediction of future system output and it is used for finding an optimal control action. Control action is always optimal according to the given criterion and constraints, which can be directly implemented into control algorithm. A feed-forward neural network with back-propagation learning algorithm is used as a system model. Obtained controller is adaptive, because the neural network moddelis able to observe system changes and adapt itself. This paper presents using of a multi-dimensional model-based predictive controller for hot air tunnel control. Two quantities of hot-air tunnel are controlled - the air flow and the temperature. These quantities are controlled in feedback control loops. The algorithm was implemented in MATLAB-Simulink and tested on the physical model. Communication between PC and hot-air tunnel is provided by PLC (connected via Ethernet). The practical
Czech name
Vícedimenzionální prediktivní řízení telovzdušného modelu s použitím neuronových sítí.
Czech description
Článek se zabývá použitím neuronových sítí při řízení vícerozměrných systémů.

Publication year
2005
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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