Predictive control based on linear models has become a mature technology in the last decade. Many successful real-time applications can be found in almost every sector of industry. Nonlinear predictive control can further increase the performance of this easy-to-understand control strategy. ; One of the main problems of implementing nonlinear predictive control is the computational aspect, which is of most importance in real-life applications. In this paper, suboptimal nonlinear predictive control strategies are proposed and compared. The nonlinear predictors are built based on neural identification methods or by white modelling. ; The use of diophantine equations, which is a common technique to calculate the optimal contribution of the noise model, is avoided by using a more natural method. The comparison between the control algorithms is made based on a simulated discrete multivariable nonlinear system and a continuous stirred tank reactor.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, volume 9, number 1 (1999) ; click here to follow the link
Biblioteka Uniwersytetu Zielonogórskiego
Sep 3, 2021
Jan 19, 2021
58
https://www.zbc.uz.zgora.pl/publication/64777
Edition name | Date |
---|---|
Suboptimal nonlinear predictive controllers | Sep 3, 2021 |
Haber, Robert Bars, Ruth Lengyel, Orsolya Kowalczuk, Zdzisław - red.
Tatjewski, Piotr Korbicz, Józef - red. Uciński, Dariusz - red.
Ahmida, Zahir Charef, Abdelfettah Becerra, Victor M. Korbicz, Józef - red. Uciński, Dariusz - red.
Marusak, Piotr M. Tatjewski, Piotr Korbicz, Józef - ed.
Deng, Jiamei Becerra, Victor M. Stobart, Richard Korbicz, Józef - ed.
Tatjewski, Piotr Ławryńczuk, Maciej Korbicz, Józef - red.
Ławryńczuk, Maciej Iacono, Mauro - ed. Kołodziej, Joanna - ed.
Marusak, Piotr M. Tatjewski, Piotr Korbicz, Józef - ed. Sauter, Dominique - ed.