# Soft computing in modelbased predictive control footnotemark

Piotr Tatjewski; Maciej Ławrynczuk

International Journal of Applied Mathematics and Computer Science (2006)

- Volume: 16, Issue: 1, page 7-26
- ISSN: 1641-876X

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topTatjewski, Piotr, and Ławrynczuk, Maciej. "Soft computing in modelbased predictive control footnotemark." International Journal of Applied Mathematics and Computer Science 16.1 (2006): 7-26. <http://eudml.org/doc/207779>.

@article{Tatjewski2006,

abstract = {The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computational properties of MPC algorithms are presented and discussed, from a neural network model of a process in standard MPC structures to modeling parts or entire MPC controllers with neural networks. Finally, a simulation example and conclusions are given.},

author = {Tatjewski, Piotr, Ławrynczuk, Maciej},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {model predictive control; neural networks; fuzzy systems; nonlinear systems; process control},

language = {eng},

number = {1},

pages = {7-26},

title = {Soft computing in modelbased predictive control footnotemark},

url = {http://eudml.org/doc/207779},

volume = {16},

year = {2006},

}

TY - JOUR

AU - Tatjewski, Piotr

AU - Ławrynczuk, Maciej

TI - Soft computing in modelbased predictive control footnotemark

JO - International Journal of Applied Mathematics and Computer Science

PY - 2006

VL - 16

IS - 1

SP - 7

EP - 26

AB - The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computational properties of MPC algorithms are presented and discussed, from a neural network model of a process in standard MPC structures to modeling parts or entire MPC controllers with neural networks. Finally, a simulation example and conclusions are given.

LA - eng

KW - model predictive control; neural networks; fuzzy systems; nonlinear systems; process control

UR - http://eudml.org/doc/207779

ER -

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