Declercq, Filip ; Keyser, Robin de
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
|Suboptimal nonlinear predictive controllers||Sep 3, 2021|
Haber, Robert Bars, Ruth Lengyel, Orsolya Kowalczuk, Zdzisław - red.
Ahmida, Zahir Charef, Abdelfettah Becerra, Victor M. Korbicz, Józef - red. Uciński, Dariusz - red.
Tatjewski, Piotr Korbicz, Józef - red. Uciński, Dariusz - red.
Tatjewski, Piotr Ławryńczuk, Maciej Korbicz, Józef - red.
Hedjar, Ramdane Toumi, Redouane Boucher, Patrick Dumur, Didier Beliczyński, Bartłomiej - red.
Mostafa, El-Sayed M.E. Korbicz, Józef - red. Uciński, Dariusz - red.
Ordys, Andrzej W. Hangstrup, Mads E. Grimble, Michael J. Korbicz, Józef - red. Uciński, Dariusz - red.
Van Den Boom, Ton J.J. De Vries, Rob A.J. Kowalczuk, Zdzisław - red.