@ARTICLE{Ogonowski_Szymon_Control_2020,
 author={Ogonowski, Szymon and Bismor, Dariusz and Ogonowski, Zbigniew},
 volume={vol. 30},
 number={No 3},
 journal={Archives of Control Sciences},
 pages={471-500},
 howpublished={online},
 year={2020},
 publisher={Committee of Automatic Control and Robotics PAS},
 abstract={Electromagnetic mill installation for dry grinding represents a complex dynamical system that requires specially designed control system. The paper presents model-based predictive control which locates closed loop poles in arbitrary places. The controller performs as gain scheduling prototype where nonlinear model – artificial recurrent neural network, is parameterized with additional measurements and serves as a basis for local linear approximation. Application of such a concept to control electromagnetic mill load allows for stable performance of the installation and assures fulfilment of the product quality as well as the optimization of the energy consumption.},
 type={Article},
 title={Control of complex dynamic nonlinear loading process for electromagnetic mill},
 URL={http://www.czasopisma.pan.pl/Content/117707/PDF/art04.pdf},
 doi={10.24425/acs.2020.134674},
 keywords={predictive control, pole placement, nonlinear dynamics, neural modelling, electromagnetic mill},
}