Creator:

Tatjewski, Piotr ; Ławryńczuk, Maciej

Contributor:

Korbicz, Józef - red.

Title:

Soft computing in model - based predictive control

Subtitle:

Soft Computing in Control and Fault Diagnosis

Group publication title:

AMCS, Volume 16 (2006)

Subject and Keywords:

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

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

Date:

2006

Resource Type:

artykuł

Pages:

7-26

Source:

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

Language:

eng

Rights:

Biblioteka Uniwersytetu Zielonogórskiego