0569998 20230316110709.620230313235959.9 10.1007/978-3-031-26474-0_8 12 s. 209 P cav_un_epca*0569489978-3-031-26474-01876-1100151-162ChamSpringer2023GusikhinO.MadaniK.NijmeijerH. output-feedback control model predictive control state estimation Bayesian methods robotic system bounded disturbances cav_un_auth*0382598 Kuklišová Pavelková Lenka UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems CZ 50 Ústav teorie informace a automatizace AV ČR, v. v. i. 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/2023/AS/kuklisova-0569998.pdf The paper deals with an algorithm of output-feedback model predictive control (MPC) where the required point state estimate is selected from the set of possible estimates. The involved state estimator is based on an approximate uniform Bayesian filter. In the paper, there are compared conservative mean and progressive composite state estimates. The proposed method is illustrated by the motion control of a specific robotic system. BC 10000 10200 10201 2023 2 RVO:67985556 https://hdl.handle.net/11104/0341355 S 49 0.148 16.03 0.31 4693 Q4 2023 SCOPUS PUBMED WOS cav_un_epca*0569489 Informatics in Control, Automation and Robotics. ICINCO 2021 : Revised Selected Papers Springer 2023 Cham 151 162 978-3-031-26474-0 Lecture Notes in Electrical Engineering 1006 1876-1100 1876-1119 Gusikhin O. 340 Madani K. 340 Nijmeijer H. 340