0536061 20250123090100.820201214235959.9 85098241378 000635651400155 10.1109/CoDIT49905.2020.9263867 6 s. P cav_un_epca*0536369978-1-7281-5954-6Proceedings of the 7th International Conference on Control, Decision and Information Technologies (CoDIT) 2020897-902PiscatawayIEEE2020 output-feedback model predictive control bounded uncertainty Bayesian state estimation parallel kinematic machine cav_un_auth*0382598 Kuklišová Pavelková Lenka UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems CZ 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/2020/AS/kuklisova-0536061.pdf GA18-15970S GA ČR CZ cav_un_auth*0362986 The paper deals with an output-feedback model predictive control (MPC) for discrete-time systems influenced by bounded disturbances. The proposed MPC combines a state-space design and a state estimation. The state estimates are obtained by a specific uniform Bayesian filter. It provides an evident disturbance attenuation in the estimated state. The MPC design considers a quadratic cost function that incorporates penalties on the tracking error, on the actuation effort and on the system output increments. The theoretical results are completed by illustrative examples using a dynamic model of a parallel kinematic machine as a controlled system. cav_un_auth*0400949 International Conference on Control, Decision and Information Technologies 2020 (CoDIT 2020) /7./ 20200629 20200702 Prague CZ BC 20000 20200 20204 2021 2 PR RVO:67985556 http://hdl.handle.net/11104/0314148 S 2020 85098241378 SCOPUS PUBMED 000635651400155 WOS cav_un_epca*0536369 Proceedings of the 7th International Conference on Control, Decision and Information Technologies (CoDIT) 2020 IEEE 2020 Piscataway 897 902 978-1-7281-5954-6 2576-3555