@ARTICLE{Nebeluk_Robert_Efficient_2020,
 author={Nebeluk, Robert and Marusak, Piotr},
 volume={vol. 30},
 number={No 2},
 journal={Archives of Control Sciences},
 pages={325-363},
 howpublished={online},
 year={2020},
 publisher={Committee of Automatic Control and Robotics PAS},
 abstract={Model predictive control (MPC) algorithms brought increase of the control system performance in many applications thanks to relatively easily solving issues that are hard to solve without these algorithms. The paper is focused on investigating how to further improve the control system performance using a trajectory of parameters weighting predicted control errors in the performance function of the optimization problem. Different shapes of trajectories are proposed and their influence on control systems is tested. Additionally, experiments checking the influence of disturbances and of modeling uncertainty on control system performance are conducted. The case studies were done in control systems of three control plants: a linear non- minimumphase plant, a nonlinear polymerization reactor and a nonlinear thin film evaporator. Three types of MPC algorithms were used during research: linear DMC, nonlinear DMC with successive linearization (NDMC–SL), nonlinear DMC with nonlinear prediction and linearization (NDMC–NPL). Results of conducted experiments are presented in greater detail for the control system of the polymerization reactor, whereas for the other two control systems only the most interesting results are presented, for the sake of brevity. The experiments in the control system of the linear plant were done as preliminary experiments with the modified optimization problem. In the case of control system of the thin film evaporator the researched mechanisms were used in the control system of a MIMO plant showing possibilities of improving the control system performance.},
 type={Article},
 title={Efficient MPC algorithms with variable trajectories of parameters weighting predicted control errors},
 URL={http://journals.pan.pl/Content/116649/PDF/art06.pdf},
 doi={10.24425/acs.2020.133502},
 keywords={model predictive control, nonlinear systems, nonlinear models, nonlinear control, simulation, optimization},
}