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) ; kliknij tutaj, żeby przejść
Biblioteka Uniwersytetu Zielonogórskiego
2021-09-03
2021-01-19
65
https://zbc.uz.zgora.pl/publication/64777
Nazwa wydania | Data |
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Suboptimal nonlinear predictive controllers | 2021-09-03 |
Haber, Robert Bars, Ruth Lengyel, Orsolya Kowalczuk, Zdzisław - red.
Tatjewski, Piotr Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
Marusak, Piotr M. Tatjewski, Piotr Korbicz, Józef (1951- ) - ed.
Ahmida, Zahir Charef, Abdelfettah Becerra, Victor M. Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
Deng, Jiamei Becerra, Victor M. Stobart, Richard Korbicz, Józef (1951- ) - ed.
Tatjewski, Piotr Ławryńczuk, Maciej Korbicz, Józef (1951- ) - red.
Ławryńczuk, Maciej Iacono, Mauro - ed. Kołodziej, Joanna - ed.
Marusak, Piotr M. Tatjewski, Piotr Korbicz, Józef (1951- ) - ed. Sauter, Dominique - ed.