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Title: Soft computing in model - based predictive control

Creator:

Tatjewski, Piotr ; Ławryńczuk, Maciej

Date:

2006

Resource Type:

artykuł

Contributor:

Korbicz, Józef (1951- ) - red.

Subtitle:

Soft Computing in Control and Fault Diagnosis

Group publication title:

AMCS, Volume 16 (2006)

Abstract:

The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) isstudied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugenotype in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural networkmodeling to improve structural or computational properties of MPC algorithms are presented and discussed, from a neuralnetwork 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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:46818

Pages:

7-26

Source:

AMCS, Volume 16, Number 1 (2006) ; click here to follow the link

Language:

eng

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Subject and keywords:

process control model predictive control nonlinear systems fuzzy systems neural networks

Object collections:

Last modified:

Nov 5, 2024

In our library since:

Aug 10, 2018

Number of object content hits:

184

All available object's versions:

https://zbc.uz.zgora.pl/publication/54909

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Soft computing in model - based predictive control Nov 5, 2024

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