Soft computing in modelbased predictive control footnotemark

• Autorzy: Piotr Tatjewski , Maciej Ławrynczuk
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2006
• Tom: 16
• Numer: 1
• Strony: 7-26

Daty

wydano

2006

otrzymano

2005-11-18

poprawiono

2005-12-21

Twórcy

autor

Piotr Tatjewski

• Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

autor

Maciej Ławrynczuk

• Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

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