TY - GEN A1 - Declercq, Filip A1 - Keyser, Robin de A2 - Kowalczuk, Zdzisław - red. PB - Zielona Góra: Uniwersytet Zielonogórski N2 - 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. N2 - 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. N2 - 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. L1 - http://www.zbc.uz.zgora.pl/Content/58162/AMCS_1999_9_1_5.pdf L2 - http://www.zbc.uz.zgora.pl/Content/58162 KW - predictive control KW - nonlinear control KW - sequential quadratic programming KW - diophantine equations T1 - Suboptimal nonlinear predictive controllers UR - http://www.zbc.uz.zgora.pl/dlibra/docmetadata?id=58162 ER -