Publication detail

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

PIVOŇKA, P., NEPEVNÝ, P.

Original Title

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

English Title

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

Type

conference paper

Language

en

Original Abstract

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 model is 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 results are discussed and advantages and disadvantages of multi-dimensional model-based predictive controller are shown.

English abstract

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 model is 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 results are discussed and advantages and disadvantages of multi-dimensional model-based predictive controller are shown.

Keywords

Model-based predictive controller, Neural network model, PLC

RIV year

2005

Released

19.09.2005

Publisher

R. Hampel

Location

Zittau

ISBN

3-9808089-6-3

Book

12th Zittau East-West Fuzzy Colloquium

Pages from

176

Pages to

181

Pages count

6

BibTex


@inproceedings{BUT15252,
  author="Petr {Pivoňka} and Petr {Nepevný}",
  title="Multi-Dimensional Predictive Control of Hot-Air Tunnel Using a Neural Network Modell",
  annote="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 model is 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 results are discussed and advantages and disadvantages of multi-dimensional
model-based predictive controller are shown.",
  address="R. Hampel",
  booktitle="12th Zittau East-West Fuzzy Colloquium",
  chapter="15252",
  institution="R. Hampel",
  year="2005",
  month="september",
  pages="176",
  publisher="R. Hampel",
  type="conference paper"
}