0517130 20250123091551.520191202235959.9 85075666718 000612994900011 10.1007/978-3-030-31993-9_11 22 s. 22 P cav_un_epca*0517111978-3-030-31992-21876-1100230-251ChamSpringer2019GusikhinO.MadaniK. discrete model predictive control nonlinear design lagrange equations articulated robots cav_un_auth*0101064 Belda Květoslav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2019/AS/belda-0517130.pdf This paper deals with a novel nonlinear design of the discrete model predictive control represented by two algorithms based on the features of linear methods for the numerical solution of ordinary differential equations. The design algorithms allow more accurate motion control of robotic or mechatronic systems that are usually modelled by nonlinear differential equations up to the second order. The proposed ways apply nonlinear models directly to the construction of equations of predictions employed in predictive control design. These equations are composed using principles of explicit linear multi-step methods leading to straightforward and unambiguous construction of the predictions. Examples of the noticeably improved behaviour of proposed ways in comparison with conventional linear predictive control are demonstrated by comparative simulations with the nonlinear model of six-axis articulated robot. BC 10000 10200 10201 2020 1 4 A sml 4as 20241106135757.6 RVO:67985556 http://hdl.handle.net/11104/0302696 S Consent Copyright 20181118 2019 Soubory v repozitáři: belda-0517130-ConsentCopyright.pdf 85075666718 SCOPUS PUBMED 000612994900011 WOS cav_un_epca*0517111 Informatics in Control, Automation and Robotics : 15th International Conference, ICINCO 2018, Porto, Portugal, July 29-31, 2018, Revised Selected Papers 978-3-030-31992-2 1876-1100 230 251 Cham Springer 2019 Lecture Notes in Electrical Engineering 613 Gusikhin O. 340 340 Madani K.