0459995 20240103212314.020160610235959.9 84991037566 000381504800162 10.1016/j.ifacol.2016.07.327 6 s. P cav_un_epca*04720952405-8963967-972TrondheimIFAC2016 model predictive control bounded noise probabilistic models linear state-space models cav_un_auth*0101175 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Pavelková Lenka UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101064 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Belda Květoslav UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2016/AS/pavelkova-0459995.pdf cav_un_auth*0331019 GA16-09848S GA AV ČR This paper concerns the model predictive control applied to the systems with bounded uncertainties. These systems are described by a state-space model with uniformly distributed states and outputs with unknown bounds of respective distributions. The model matrices are assumed to be known. The approximate estimation of states and noise bounds is based on the Bayesian approach. A state-space generalised predictive control is selected as a suitable target model predictive control strategy. The proposed concept of the above mentioned estimation within generalised predictive control is illustrated by representative comparative simulation examples. cav_un_auth*0331299 IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS-CAB) /11./ 20160606 20160608 Trondheim NO BC 2017 2 PR RVO:67985556 http://hdl.handle.net/11104/0260441 S n.a. Proceedings Paper Automation Control Systems 0.298 Q3 Q3 2016 84991037566 SCOPUS 000381504800162 WOS cav_un_epca*0472095 IFAC-PapersOnLine. Volume 49, Issue 7 - 11th IFAC Symposium on Dynamics and Control of Process SystemsIncluding Biosystems DYCOPS-CAB 2016 2405-8963 967 972 Trondheim IFAC 2016